Information About the Dataset:
The dataset The goal of this project is to understand the influence of the parents background, test preparation, and various other variables on the students math score.
There are 8 independent variables:
gender: Sex of a student (Male/Female)race/ethnicity: Ethnicity of a student (Group A,B,C,D,E)parental level of education: parents' final education (bachelor's degree,some college,master's degree,associate's degree,high school)lunch: What type of lunch the student had before test (standard or free/reduced)test preparation course: Whether the student completed any preparation course before the test.reading score: Reading score obtained by the student.writing score: Writing score obtained by the student.
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Data Ingestion :
- In Data Ingestion phase the data is first read as csv.
- Then the data is split into training and testing and saved as csv file.
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Data Transformation :
- Implemented SimpleImputer with a median strategy for handling outliers in Numeric Variables, followed by standard scaling.
- Applied SimpleImputer with a most frequent strategy for Categorical Variables, performed one-hot encoding, and scaled the data using a standard scaler.
- Saved the resulting preprocessor as a pkl file in the artifacts folder.
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Model Training :
- Conducted training and evaluation for all models in this phase, with Linear Regression identified as the optimal model.
- Subsequently, performed hyperparameter tuning to enhance model performance.
- The finalized model is saved as a pickle file for utilization in the prediction pipeline.
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Prediction Pipeline :
- Developed a pipeline that transforms input data into a dataframe and incorporates functions for loading pickle files and predicting final results in Python.
- Flask App creation :
- Built a Flask app with User Interface to predict the math score of a student given the required features inside a Web Application.
python application.py