flaskfiles

Docker

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+# Use lightweight Python image
+FROM python:3.10-slim
+
+# Set working directory inside container
+WORKDIR /app
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy project files
+COPY . .
+
+# Expose Flask port
+EXPOSE 5000
+
+# Run Flask application
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify, send_from_directory
+from flask_cors import CORS
+from model import predict_exam_score
+import os
+
+app = Flask(__name__, static_folder="../frontend")
+CORS(app)
+
+# Serve frontend
+@app.route("/")
+def serve_frontend():
+    return send_from_directory(app.static_folder, "index.html")
+
+# Prediction API
+@app.route("/predict", methods=["POST"])
+def predict():
+    data = request.get_json()
+    prediction = predict_exam_score(data)
+    return jsonify({"predicted_score": round(prediction, 2)})
+
+if __name__ == "__main__":
+    app.run(debug=True)

index.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Student Performance Prediction</title>
+    <link rel="stylesheet" href="style.css">
+</head>
+<body>
+
+<div class="container">
+<h2>Student Performance Prediction</h2>
+
+<label>Hours Studied:</label>
+<input type="number" id="Hours_Studied">
+
+<label>Attendance (%):</label>
+<input type="number" id="Attendance">
+
+<label>Previous Score:</label>
+<input type="number" id="Previous_Scores">
+
+<label>Sleep Hours:</label>
+<input type="number" id="Sleep_Hours">
+
+<label>Motivation Level (0–Low, 1–Medium, 2–High):</label>
+<input type="number" id="Motivation_Level">
+
+<label>Internet Access (0–No, 1–Yes):</label>
+<input type="number" id="Internet_Access">
+
+<label>Gender (0–Female, 1–Male):</label>
+<input type="number" id="Gender">
+
+<button onclick="predict()">Predict Exam Score</button>
+
+<p id="result"></p>
+</div>
+
+<script>
+function predict() {
+    document.getElementById("result").innerText = "Predicting...";
+
+    fetch("http://127.0.0.1:5000/predict", {
+        method: "POST",
+        headers: {
+            "Content-Type": "application/json"
+        },
+        body: JSON.stringify({
+            "Hours_Studied": Number(Hours_Studied.value),
+            "Attendance": Number(Attendance.value),
+            "Parental_Involvement": 2,
+            "Access_to_Resources": 2,
+            "Extracurricular_Activities": 1,
+            "Sleep_Hours": Number(Sleep_Hours.value),
+            "Previous_Scores": Number(Previous_Scores.value),
+            "Motivation_Level": Number(Motivation_Level.value),
+            "Internet_Access": Number(Internet_Access.value),
+            "Tutoring_Sessions": 1,
+            "Family_Income": 2,
+            "Teacher_Quality": 2,
+            "School_Type": 1,
+            "Peer_Influence": 1,
+            "Physical_Activity": 1,
+            "Learning_Disabilities": 0,
+            "Parental_Education_Level": 2,
+            "Distance_from_Home": 5,
+            "Gender": Number(Gender.value)
+        })
+    })
+    .then(res => res.json())
+    .then(data => {
+        result.innerText = "Predicted Exam Score: " + data.predicted_score;
+    });
+}
+</script>
+
+</body>
+</html>

model.py

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+# ===============================
+# Student Performance Prediction
+# ===============================
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+# -------------------------------
+# 1. Load Dataset (USE YOUR PATH)
+# -------------------------------
+data = pd.read_csv(
+    r"C:\Users\tmeka\Downloads\StudentPerformanceFactors.csv"
+)
+
+print("Dataset loaded:", data.shape)
+
+# -------------------------------
+# 2. Encode Categorical Columns
+# -------------------------------
+for col in data.columns:
+    if data[col].dtype == "object":
+        data[col] = LabelEncoder().fit_transform(data[col])
+
+# -------------------------------
+# 3. Split Features & Target
+# -------------------------------
+X = data.drop("Exam_Score", axis=1)   # 19 features
+y = data["Exam_Score"]
+
+print("Number of input features:", X.shape[1])
+print("Feature names:\n", X.columns)
+
+# -------------------------------
+# 4. Train-Test Split
+# -------------------------------
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# -------------------------------
+# 5. Train Random Forest Model
+# -------------------------------
+model = RandomForestRegressor(
+    n_estimators=100,
+    random_state=42
+)
+model.fit(X_train, y_train)
+
+print("Model trained successfully")
+
+# -------------------------------
+# 6. Evaluate Model
+# -------------------------------
+y_pred = model.predict(X_test)
+
+print("\nModel Performance:")
+print("MAE:", mean_absolute_error(y_test, y_pred))
+print("MSE:", mean_squared_error(y_test, y_pred))
+print("R2 :", r2_score(y_test, y_pred))
+
+# -------------------------------
+# 7. Predict for New Student
+# -------------------------------
+# MUST PROVIDE ALL 19 FEATURES
+new_student = {
+    "Hours_Studied": 5,
+    "Attendance": 90,
+    "Parental_Involvement": 2,
+    "Access_to_Resources": 2,
+    "Extracurricular_Activities": 1,
+    "Sleep_Hours": 7,
+    "Previous_Scores": 85,
+    "Motivation_Level": 2,
+    "Internet_Access": 1,
+    "Tutoring_Sessions": 1,
+    "Family_Income": 2,
+    "Teacher_Quality": 2,
+    "School_Type": 1,
+    "Peer_Influence": 1,
+    "Physical_Activity": 1,
+    "Learning_Disabilities": 0,
+    "Parental_Education_Level": 2,
+    "Distance_from_Home": 5,
+    "Gender": 1
+}
+
+new_student_df = pd.DataFrame([new_student])
+
+prediction = model.predict(new_student_df)
+
+print("\nPredicted Exam Score:", round(prediction[0], 2))

scripts.js

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+async function predictScore() {
+
+    const data = {
+        Hours_Studied: Number(document.getElementById("Hours_Studied").value),
+        Attendance: Number(document.getElementById("Attendance").value),
+        Previous_Scores: Number(document.getElementById("Previous_Scores").value),
+        Gender: Number(document.getElementById("Gender").value),
+        Parental_Education_Level: Number(document.getElementById("Parental_Education_Level").value),
+        Motivation_Level: Number(document.getElementById("Motivation_Level").value),
+        Internet_Access: Number(document.getElementById("Internet_Access").value),
+        School_Type: Number(document.getElementById("School_Type").value)
+    };
+
+    const response = await fetch("http://127.0.0.1:5000/predict", {
+        method: "POST",
+        headers: {
+            "Content-Type": "application/json"
+        },
+        body: JSON.stringify(data)
+    });
+
+    const result = await response.json();
+
+    if (result.predicted_score !== undefined) {
+        document.getElementById("result").innerText =
+            "Predicted Exam Score: " + result.predicted_score;
+    } else {
+        document.getElementById("result").innerText =
+            "Error: " + result.error;
+    }
+}

style.css

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+body {
+    font-family: Arial, Helvetica, sans-serif;
+    background-color: #f4f6f8;
+}
+
+.container {
+    width: 360px;
+    margin: 60px auto;
+    background: #ffffff;
+    padding: 25px;
+    border-radius: 10px;
+    box-shadow: 0px 0px 12px rgba(0,0,0,0.15);
+}
+
+h2 {
+    text-align: center;
+    margin-bottom: 20px;
+}
+
+label {
+    font-size: 14px;
+    font-weight: bold;
+}
+
+input {
+    width: 100%;
+    padding: 7px;
+    margin-top: 4px;
+    margin-bottom: 12px;
+}
+
+button {
+    width: 100%;
+    padding: 10px;
+    background-color: #007bff;
+    color: white;
+    border: none;
+    font-size: 16px;
+    cursor: pointer;
+    border-radius: 5px;
+}
+
+button:hover {
+    background-color: #0056b3;
+}
+
+#result {
+    margin-top: 15px;
+    font-size: 18px;
+    color: green;
+    text-align: center;
+    font-weight: bold;
+}