Leverage advanced machine learning algorithms to analyze student data and predict academic performance. Help educators make data-driven decisions to improve outcomes.
Comprehensive machine learning tools designed specifically for educational institutions
Advanced algorithms analyze 9 key factors including study hours, attendance, past scores, and extracurricular activities to predict final exam performance.
Beautiful visualizations that make complex data easy to understand. Track student progress, identify at-risk students, and monitor class performance.
Automatically identify students at risk of failing. Get instant alerts when a student's predicted score drops below passing threshold.
Create detailed student profiles with comprehensive analytics. Understand how different factors correlate with academic success.
Enterprise-grade security with end-to-end encryption. FERPA compliant with role-based access control for sensitive student data.
Full-featured Python API for custom integrations. Build your own prediction pipelines with our easy-to-use SDK.
Import your student data via CSV, Excel, or API. We support all standard formats including gender, study hours, attendance, past scores, and more.
Our ML models automatically process the data, identifying patterns and correlations that predict final exam performance with 95% accuracy.
Receive detailed predictions, risk assessments, and actionable recommendations to help students improve their performance.
See the prediction model in action with sample student data
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
# Load student data
df = pd.read_csv('student_performance.csv')
# Features for prediction
features = ['Study_Hours_per_Week', 'Attendance_Rate',
'Past_Exam_Scores', 'Internet_Access_at_Home',
'Extracurricular_Activities']
# Encode categorical variables
le = LabelEncoder()
df['Internet_Access_at_Home'] = le.fit_transform(
df['Internet_Access_at_Home'])
df['Extracurricular_Activities'] = le.fit_transform(
df['Extracurricular_Activities'])
# Prepare data
X = df[features]
y = df['Pass_Fail']
# Split and train
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
# Train Random Forest model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make prediction for new student
new_student = [[31, 92.1, 86, 1, 1]] # Study hrs, Attendance, Past score
prediction = model.predict(new_student)
probability = model.predict_proba(new_student)
print(f"Prediction: {prediction[0]}")
print(f"Confidence: {probability[0].max():.2%}")"PredictEd has transformed how we approach student success. The early warning system helped us identify at-risk students weeks before exams, allowing for timely interventions."
"The accuracy is remarkable. We've seen a 23% improvement in pass rates since implementing PredictEd's recommendations. It's like having a data scientist on staff."
"The Python API made integration seamless. We connected PredictEd to our existing LMS in just two days. The documentation is excellent and support is responsive."
"Finally, a tool that understands education data. The feature importance analysis helped us discover that attendance matters more than we thought. Game changer!"
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