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annotations_creators:
- machine-generated
language:
- en
license:
- mit
task_categories:
- tabular-regression
tags:
- education
- machine-learning
- regression
- student-performance
- mongodb
size_categories:
- 1K<n<10K
---
# Student Performance Prediction Using Python, MongoDB, and Scikit-Learn
A beginner-friendly Machine Learning project for B.Tech Computer Science and Engineering (CSE) students.
---
## 📌 Project Overview
- **Objective:** Predict students' final examination marks out of 100 based on study hours, attendance, previous marks, and assignment completion.
- **Database:** MongoDB (`student_performance_db`)
- **Dataset:** 1,000 realistic student records
- **Python Driver:** PyMongo
- **Machine Learning Model:** Linear Regression
- **Evaluation Metrics:** $R^2$ Score (96.1%) and Mean Absolute Error (2.04 marks)
---
## 📂 Project Structure
```text
ca2 mdb/
│
├── main.py # Complete all-in-one executable script
├── step1_insert_to_mongodb.py # Step 1: Generate dataset & insert into MongoDB
├── step2_train_and_predict.py # Step 2: Query MongoDB, train model, evaluate & predict
├── Student_Performance_Prediction.ipynb # Jupyter Notebook implementation
├── requirements.txt # Required Python packages
├── student_data.csv # Generated dataset backup
├── actual_vs_predicted.png # High-resolution output evaluation plot
├── PROJECT_REPORT.md # Full academic project report (8 sections)
├── VIVA_QUESTIONS.md # Viva / oral exam preparation guide
└── README.md # Project instructions and quickstart
```
---
## ⚙️ Prerequisites & Installation
### 1. Ensure MongoDB Server is Running
If you have MongoDB Community Server installed on Windows:
- MongoDB runs as a background service on port `27017`.
- You can verify with PowerShell:
```powershell
Get-Service -Name "*mongo*"
```
If stopped, start it with:
```powershell
net start MongoDB
```
### 2. Install Required Python Libraries
Open your terminal / command prompt in this project folder and run:
```bash
pip install -r requirements.txt
```
*(Or install individually: `pip install pymongo pandas numpy scikit-learn matplotlib`)*
---
## 🚀 How to Run the Project
### Option A: Run the Complete Pipeline (Recommended)
Run the all-in-one script:
```bash
python main.py
```
This script will:
1. Generate 1,000 realistic student records.
2. Connect to MongoDB and insert all 1,000 documents.
3. Retrieve records from MongoDB using PyMongo.
4. Display 5 sample records.
5. Preprocess the dataset and split it 80/20 into train/test sets.
6. Train a Linear Regression model.
7. Print $R^2$ Score, MAE, and RMSE.
8. Display a table of Actual vs Predicted Marks for sample students.
9. Save visualization graphs to `actual_vs_predicted.png`.
10. Test prediction on a custom student profile.
---
### Option B: Run Modular Scripts Step-by-Step
**Step 1: Populate MongoDB**
```bash
python step1_insert_to_mongodb.py
```
**Step 2: Train Model & Evaluate**
```bash
python step2_train_and_predict.py
```
---
### Option C: Jupyter Notebook / VS Code Interactive
Open `Student_Performance_Prediction.ipynb` in VS Code or Jupyter Lab, and run the cells sequentially.
---
## 📊 Expected Output Summary
```text
==================================================
STEP 3: FETCHING DATA FROM MONGODB USING PYMONGO
==================================================
-> Successfully fetched 1000 records from MongoDB.
--- 5 Sample Records Retreived from MongoDB ---
_id student_id study_hours attendance previous_marks assignments_score final_marks
6ae78958eca66c4748b15e77 STU1001 4.2 67.2 54.4 6 70.2
6ae78958eca66c4748b15e78 STU1002 9.1 81.1 53.6 4 81.3
6ae78958eca66c4748b15e79 STU1003 7.2 94.0 89.8 8 100.0
6ae78958eca66c4748b15e7a STU1004 6.1 88.6 53.7 10 84.1
6ae78958eca66c4748b15e7b STU1005 2.3 91.5 55.0 9 67.2
==================================================
STEP 6: MODEL EVALUATION METRICS
==================================================
1. R-Squared (R2) Score : 0.9610 (96.10% variance explained)
2. Mean Absolute Error : 2.04 marks
3. Root Mean Sq. Error : 2.53 marks
==================================================
STEP 7: ACTUAL VS PREDICTED RESULTS (FIRST 10 TEST STUDENTS)
==================================================
Actual Marks Predicted Marks Difference (Error)
0 66.7 66.1 0.6
1 86.9 89.4 2.5
2 76.9 77.5 0.6
3 73.3 76.3 3.0
4 81.4 80.2 1.2
5 86.6 83.3 3.3
6 58.2 60.9 2.7
7 83.9 81.6 2.3
8 65.3 63.4 1.9
9 69.4 72.5 3.1
```
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