Create app.py

app.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, r2_score
+
+# Load your dataset
+df = pd.read_excel("Book 1 (1).xlsx")
+
+# Select features (X) and target (y)
+# You can adjust this list depending on what you want to use
+features = ["Attendance", "Hours studied", "Quizzes_avg", "Confidence"]
+X = df[features]
+y = df["Final Grade"]
+
+# Split data into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Evaluate
+print("Mean Absolute Error:", mean_absolute_error(y_test, y_pred))
+print("R² Score:", r2_score(y_test, y_pred))
+
+# Example: predict grade for a new student
+sample = pd.DataFrame([{
+    "Attendance": 0.95,
+    "Hours studied": 12,
+    "Quizzes_avg": 85,
+    "Confidence": 7
+}])
+print("Predicted grade:", model.predict(sample)[0])