This project focuses on selecting the best machine learning (ML) model for predicting student scores. It serves as a learning milestone, providing insights into various ML models, their evaluation, and practical application in predictive analytics. This project is not intended to fulfill a specific business requirement but rather to enhance understanding and skills in ML model selection and performance evaluation.
- To explore and compare different machine learning models.
- To evaluate the performance of each model based on relevant metrics.
- To select the best-performing model for predicting student scores.
- To implement the chosen model and make predictions.
The project is organized into the following sections:
- Data Collection and Preprocessing: Gathering the dataset and preparing it for analysis.
- Exploratory Data Analysis (EDA): Understanding the data through visualization and summary statistics.
- Model Selection: Evaluating multiple ML models to identify the best one.
- Model Evaluation: Assessing the models using various metrics to ensure robustness.
- Prediction: Using the selected model to predict student scores.
- Conclusion: Summarizing the findings and reflecting on the learning outcomes.
The dataset used in this project includes information about students and their academic performance. Key features may include:
- Student demographics (age, race_ethnicity, etc.)
- Academic records (other subject scores etc.)
- Study habits and resources
Several machine learning models are considered in this project, including but not limited to:
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- Gradient Boosting Regressor
The models are evaluated using the following metric:
- R-squared Score (R²)
The project utilizes the following tools and libraries:
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- Jupyter Notebook