Update README.md

Machine Learning and Data Science/Basic/Autism Identification System/README.md

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-<img height="27" src="https://img.shields.io/badge/Autism Identification System -Level Basic-red.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
+<img height="27" src="https://img.shields.io/badge/Autism Identification System -Level Intermediate-red.svg?&style=for-the-badge&logo=TheSparksFoundation&logoColor=blue"/>
 
 <br> 
 
 ![](https://img.shields.io/badge/Programming_Language-Python-blue.svg)
 ![](https://img.shields.io/badge/Main_Tool_Used-Jupyter_Notebook-orange.svg)
 ![](https://img.shields.io/badge/Status-Complete-green.svg)
 
-## Autism Identification System
+# Autism Identification System
 
-### Problem Statement:
+## Problem Statement
 - **Autistic Spectrum Disorder (ASD)** is a neurodevelopmental condition that incurs significant healthcare costs. Early diagnosis can substantially reduce these costs and improve patient outcomes.
 - Current ASD diagnostic procedures involve long wait times and are not cost-effective.
 - There is a need for a **time-efficient and accessible ASD screening tool** to assist healthcare professionals and guide individuals toward pursuing formal clinical diagnosis.
 - The objective is to leverage **machine learning techniques** to create a faster and more effective screening process.
 
-### Tools and Algorithms:
+## Tools and Algorithms
 - You can use any tool of your choice (Python/R/Tableau/PowerBI/Excel/SAP/SAS).
 - **Programming Language Used**: Python
 - **Main Tool Used**: Jupyter Notebook
@@ -25,34 +25,63 @@
   - Support Vector Machine (SVM)
   - Random Forest Classifier
 
-### Dataset:
-- The dataset for this project can be found here: [Dataset.csv](Machine Learning and Data Science/Intermediate/Autism Identification System/Data.csv)
-
-### Solution:
-- The project notebook can be accessed here: [Autism Identification System](Machine Learning and Data Science/Intermediate/Autism Identification System/autism_identification_notebook.ipynb)
-
-### Project Details:
-
-1. **Introduction**:
-   - This project aims to develop an ASD screening tool using machine learning techniques to provide quick and accurate predictions.
-
-2. **Data Exploration and Preprocessing**:
-   - Load and explore the dataset.
-   - Handle missing values and encode categorical variables.
-   - Split the data into training and testing sets.
-
-3. **Model Development**:
-   - Implement and train three machine learning models: Logistic Regression, SVM, and Random Forest.
-   - Evaluate the models using appropriate metrics such as accuracy, precision, recall, and F1-score.
-
-4. **Model Evaluation**:
-   - Compare the performance of the models to determine the best one for ASD screening.
-
-5. **Conclusion**:
-   - Summarize the findings and highlight the model that performs the best.
-   - Discuss the potential impact of the screening tool in real-world healthcare settings.
-
-### Contact:
+## Dataset
+- The dataset for this project is included in the repository: [Data.csv](Data.csv)
+
+### Dataset Description
+The dataset contains information related to ASD screening. Below are the key features included in the dataset:
+
+- `A1_Score`: Score for question A1 (binary: 0, 1)
+- `A2_Score`: Score for question A2 (binary: 0, 1)
+- `A3_Score`: Score for question A3 (binary: 0, 1)
+- `A4_Score`: Score for question A4 (binary: 0, 1)
+- `A5_Score`: Score for question A5 (binary: 0, 1)
+- `A6_Score`: Score for question A6 (binary: 0, 1)
+- `A7_Score`: Score for question A7 (binary: 0, 1)
+- `A8_Score`: Score for question A8 (binary: 0, 1)
+- `A9_Score`: Score for question A9 (binary: 0, 1)
+- `A10_Score`: Score for question A10 (binary: 0, 1)
+- `age`: Age of the individual
+- `gender`: Gender of the individual
+- `ethnicity`: Ethnicity of the individual
+- `jundice`: Whether the individual had jaundice at birth (binary: yes, no)
+- `austim`: Whether the individual has a family history of autism (binary: yes, no)
+- `contry_of_res`: Country of residence
+- `used_app_before`: Whether the individual has used an app for autism screening before (binary: yes, no)
+- `result`: Result of the screening test (binary: 0, 1)
+- `age_desc`: Age description
+- `relation`: Relationship status
+
+## Solution
+- The project notebook can be accessed here: [Autism Identification System](autism_identification_notebook.ipynb)
+
+## Project Details
+
+### 1. Introduction
+This project aims to develop an ASD screening tool using machine learning techniques to provide quick and accurate predictions.
+
+### 2. Data Exploration and Preprocessing
+- **Loading and Exploring the Dataset**: Initial exploration to understand the structure and summary statistics of the dataset.
+- **Handling Missing Values**: Strategies to deal with any missing data in the dataset.
+- **Encoding Categorical Variables**: Converting categorical variables into a format that can be used by machine learning algorithms.
+- **Splitting the Data**: Dividing the dataset into training and testing sets to evaluate the model performance.
+
+### 3. Model Development
+- **Logistic Regression**: Implementation and training of a logistic regression model.
+- **Support Vector Machine (SVM)**: Implementation and training of an SVM model.
+- **Random Forest Classifier**: Implementation and training of a random forest classifier model.
+- **Hyperparameter Tuning**: Techniques to optimize the performance of the models.
+
+### 4. Model Evaluation
+- **Performance Metrics**: Evaluating the models using metrics such as accuracy, precision, recall, and F1-score.
+- **Comparison of Models**: Comparing the performance of the models to determine the best one for ASD screening.
+
+### 5. Conclusion
+- **Summary of Findings**: Highlighting the key findings and results from the models.
+- **Best Performing Model**: Identifying the model that performs the best in terms of predictive accuracy and reliability.
+- **Impact**: Discussing the potential impact of the screening tool in real-world healthcare settings and its benefits for early diagnosis of ASD.
+
+## Contact
 If you have any queries or suggestions, feel free to reach out to me.
 
 [<img height="30" src="https://img.shields.io/badge/linkedin-blue.svg?&style=for-the-badge&logo=linkedin&logoColor=white" />][LinkedIn]