Student Performance Analytics
1. Introduction
Objective: Develop a predictive model to forecast student grades based on their
study habits and other behavioral data.
Purpose: Provide insights into factors affecting academic performance and
enable targeted interventions for student success.
2. Project Workflow
1. Problem Definition:
- Predict student grades based on
study habits and related features.
- Key questions:
- What factors influence student
performance the most?
- How accurately can we predict
final grades?
2. Data Collection:
- Source: Public datasets such as UCI
Student Performance Dataset.
- Example fields: Study Time,
Attendance, Parental Support, Past Grades.
3. Data Preprocessing:
- Handle missing values, encode
categorical variables, and normalize data.
4. Model Development:
- Use regression or classification
models depending on the grade representation.
5. Model Evaluation:
- Evaluate model performance using
metrics like Mean Squared Error (MSE) or Accuracy.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Data Visualization: Matplotlib,
Seaborn
- Machine Learning Models: Scikit-learn
- Model Evaluation: Scikit-learn
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn
```
Step 2: Load and Explore Data
Load the student performance dataset:
```
import pandas as pd
data = pd.read_csv("student_performance_data.csv")
print(data.head())
```
Explore key statistics and visualize correlations:
```
import seaborn as sns
sns.heatmap(data.corr(), annot=True, cmap="coolwarm")
```
Step 3: Preprocess Data
Handle missing values and encode categorical variables:
```
data = data.dropna() # Drop rows with
missing values
# Encode categorical variables
data = pd.get_dummies(data, drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
numeric_features = ['StudyTime', 'Absences', 'PastGrades']
data[numeric_features] = scaler.fit_transform(data[numeric_features])
```
Step 4: Build Prediction Model
Split the data into training and testing sets:
```
from sklearn.model_selection import train_test_split
X = data.drop('FinalGrade', axis=1)
y = data['FinalGrade']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
```
Train a Regression model (for continuous grades):
```
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
```
For classification (if grades are categorical):
```
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)
```
Step 5: Evaluate Model
Evaluate the model using relevant metrics:
For regression:
```
from sklearn.metrics import mean_squared_error, r2_score
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R-squared:", r2)
```
For classification:
```
from sklearn.metrics import accuracy_score, classification_report
y_pred_clf = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred_clf)
print("Accuracy:", accuracy)
print(classification_report(y_test, y_pred_clf))
```
5. Expected Outcomes
1. A trained predictive model capable of estimating student grades based on
input features.
2. Insights into the most significant factors affecting student performance.
3. Evaluation metrics to gauge model accuracy and reliability.
6. Additional Suggestions
- Feature Importance:
- Use feature importance scores to
understand the key drivers of academic success.
- Deployment:
- Develop a dashboard to allow teachers
or counselors to input student data and receive predictions.
- Continuous Improvement:
- Update the model with new data
regularly to enhance accuracy.