Linear Regression model to predict the chance of admission to IVY league college
See LIVE demo here
├── LICENSE <- Open-source license if one is chosen
├── Makefile <- Makefile with convenience commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.cfg <- Configuration file for flake8
|
|__ EDA.ipynb <- EDA notebook
|
|__ src/train.py <- training script
|
|__ streamlit_app.py <- streamlit app
An education institute has recently launched a dataset that contains the details of students who have applied for admission to IVY League College. The Jamboree team wants to know what factors are important for a student's success in getting into an IVY league college. They also want to see if we can make a predictive model to predict the chance of admission to IVY league college using the given features.
Dataset
This dataset contains the details of 500 students who have applied for admission to IVY League College along with their success rate.
Summary of sanitized data:
| Column | Description |
|---|---|
serial_no |
Unique row ID |
gre_score |
Out of 340 |
toefl_score |
Out of 120 |
university_rating |
Out of 5 |
sop |
Out of 5 |
lor |
Out of 5 |
cgpa |
Out of 10 |
research |
Either 0 or 1 |
chance_of_admit |
Ranging from 0 to 1 |
Additional feature engineered columns:
| Column | Description |
|---|---|
gre_sqr |
Square of gre_score |
cgpa_sqr |
Square of cgpa |
uni_rating_sqr |
Square of university_rating |
gre_uni_ratio |
gre_score/university_rating |
cgpa_uni_ratio |
cgpa/university_rating |
gre_cgpa_prod |
gre_score*cgpa |
gre_avg_uni_rating |
Avg gre_score grouped by university_rating |
cgpa_avg_uni_rating |
Avg cgpa grouped by university_rating |
Aim:
- To analyze what factors are important for a student's success in getting into an IVY league college.
- To make a predictive interpretable model to predict the chance of admission (
chance_of_admit) to IVY league college using the given features.
Methods and Techniques used: EDA, feature engineering, modeling using sklearn pipelines, hyperparameter tuning
Measure of Performance and Minimum Threshold to reach the business objective: MSE of 1% or less with max VIF less than 5
Assumptions
- This fairly small dataset (500 entries) is representative of the real-world population.
- The data is stable and does not change over time. Thus model is assumed to not decay.
- The best model is Linear Regression with
- MSE:
0.3421% - Accuracy(R^2)
82.282% - VIF:
4.26
- MSE:
- The following features with weights are selected by the model which signifies the importance of those features Intercept: -0.25633387548676145
| Features | Coefficients |
|---|---|
cgpa |
0.599586 |
research |
0.205412 |
gre_score |
0.122329 |
toefl_score |
0.097854 |
university_rating |
0.068220 |
- CGPA has the most weight for predicting the chance of admit to IVY league college followed by research and GRE score