docs: Add comprehensive documentation and dataset information for Medical Appointment No-Shows

This PR includes detailed documentation of the Medical Appointment No-Shows project with dataset features, preprocessing steps, and project structure information for better understanding of the project.

Author: Gokul45-45

projects/detection/Medical_Appointment_No_Shows/README.md

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+# Medical Appointment No-Shows Prediction
+
+## Overview
+This project implements a machine learning model to predict whether a patient will miss their medical appointment. The model uses various patient features and appointment details to make accurate predictions.
+
+## Dataset
+The dataset contains the following information:
+- **Total Records**: Approximately 100,000 medical appointments
+- **Features**: 14 different features including patient demographics and appointment details
+- **Target Variable**: `No-show` (1 = No-show, 0 = Show)
+
+### Dataset Features
+1. `PatientId` - Unique patient identifier
+2. `AppointmentID` - Unique appointment identifier
+3. `Gender` - Patient gender (M/F)
+4. `ScheduledDay` - Date when appointment was scheduled
+5. `AppointmentDay` - Actual date of appointment
+6. `Age` - Patient age in years
+7. `Neighbourhood` - Neighbourhood where patient is from
+8. `Scholarship` - Indicates if patient is on any scholarship program
+9. `Hipertension` - Indicates if patient has hypertension
+10. `Diabetes` - Indicates if patient has diabetes
+11. `Alcoholism` - Indicates if patient has alcoholism
+12. `HandiCap` - Indicates if patient has any handicap
+13. `SMS_received` - Indicates if appointment reminder SMS was sent
+14. `No-show` - Target variable (0 = Showed up, 1 = No-show)
+
+## Files in This Project
+- `model.py` - Main model implementation with preprocessing pipeline and training code
+- `requirements.txt` - Python dependencies
+- `README.md` - This documentation file
+
+## Data Preprocessing
+- Missing values are handled by filling with mean values for numerical columns
+- Categorical variables are encoded using LabelEncoder
+- Numerical features are scaled using StandardScaler for normalization
+
+## Requirements
+- Python 3.7+
+- pandas
+- numpy
+- scikit-learn
+
+## Usage
+See `model.py` for implementation details and usage examples.