College Admission Predictor in PHP

This guide provides a comprehensive approach to creating a College Admission Predictor in PHP. The predictor uses various input criteria to estimate the probability of a student's admission into a college, leveraging machine learning techniques to deliver accurate predictions.

System Overview

The College Admission Predictor includes the following features:

• Student Profile Input: Collect and store student data such as academic performance, test scores, and extracurricular activities.
• Predictive Model Integration: Integrate a machine learning model to predict admission chances based on input data.
• Result Display: Show the predicted admission probability to the user with relevant insights and recommendations.

Implementation Guide

Follow these steps to develop the College Admission Predictor:

1. Define Requirements and Collect Data

   Determine the input criteria for prediction and collect historical data to train the machine learning model. Key data points include academic grades, standardized test scores, and other relevant factors.

2. Build and Train the Predictive Model

   Use machine learning techniques to build and train a model that can predict admission chances. Consider using libraries such as Scikit-learn or TensorFlow for model training.

   
                           # Example Python code for training a prediction model
                           import pandas as pd
                           from sklearn.model_selection import train_test_split
                           from sklearn.ensemble import RandomForestClassifier
                           from sklearn.metrics import accuracy_score
   
                           # Load and preprocess data
                           data = pd.read_csv('admission_data.csv')
                           X = data[['grades', 'test_scores', 'activities']]
                           y = data['admitted']
   
                           # Split data into training and test sets
                           X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
   
                           # Train model
                           model = RandomForestClassifier()
                           model.fit(X_train, y_train)
   
                           # Evaluate model
                           predictions = model.predict(X_test)
                           accuracy = accuracy_score(y_test, predictions)
                           print(f'Accuracy: {accuracy}')
                       

3. Integrate Predictive Model with PHP

   Export the trained model and integrate it with the PHP application. Use PHP to handle user input and interact with the model for predictions.

   
                           // Example PHP code to interact with the predictive model
                           $studentData = [
                               'grades' => $_POST['grades'],
                               'test_scores' => $_POST['test_scores'],
                               'activities' => $_POST['activities']
                           ];
   
                           // Send data to the prediction model (e.g., via API or local execution)
                           $url = 'http://localhost:5000/predict';
                           $options = [
                               'http' => [
                                   'header'  => "Content-type: application/json\r\n",
                                   'method'  => 'POST',
                                   'content' => json_encode($studentData),
                               ],
                           ];
                           $context  = stream_context_create($options);
                           $result = file_get_contents($url, false, $context);
                           echo 'Predicted Admission Probability: ' . $result;
                       

4. Develop User Interface

   Create a user-friendly interface for students to input their data and view the prediction results. Ensure the form collects all necessary information for accurate predictions.

   
                           
                           <form action="predict.php" method="post">
                               <label for="grades">Grades:</label>
                               <input type="text" id="grades" name="grades" required>
                               
                               <label for="test_scores">Test Scores:</label>
                               <input type="text" id="test_scores" name="test_scores" required>
                               
                               <label for="activities">Extracurricular Activities:</label>
                               <input type="text" id="activities" name="activities" required>
                               
                               <button type="submit">Predict Admission Chances</button>
                           </form>
                       

5. Testing and Deployment

   Thoroughly test the application to ensure accuracy and reliability of predictions. Deploy the application on a web server and ensure it is secure and scalable.