two folders for two mini projects

mlflow_data_versioning:
- Step-1_text_extractor.ipynb
- Step-2_llm_qna_data_generator.ipynb

README.md

@@ -1,50 +1,5 @@
-# MLOPs - Student Exam Results
-Toy example for predicting Student Exam Results
+# MLOPs
 
+- MLOPs - Student Exam Results
+- MLOPs - MLflow mini project
 
-### Intro
-
-The `student_exam_result.py` contains all the definitions and classes for the _Student Exam Result_ project.
-
-The notebook `Student_Exam_Result.ipynb` is used to demonstrate the usage of the code.
-
-Below, we use the same dataset to build multiple models (_LogisticRegression_) each responsible for predicting categorical encoded values of:
-- `MatchScore`
-- `WritingScore`
-- `ReadingScore`
-
-
-### `StudentExamResultFramework` class
-This class is the main class for the framework. It has a main method `run_pipeline()` which runs the whole system pipeline including:
-- Analyzing data
-- Cleaning data
-- Splitting dataset to train and test sets
-- Training the model
-- Evaluating the model on the test data
-- Run Explainable AI module on the output model using `shapely`
-- Encrypts/Decrypts the dataset
-
-#### Constructor
-The constructor takes the student exam result CSV dataset path (`csv_file_path`), and number of bins for the target score we want to predict (`nb_bins`).
-
-#### `run_pipeline()`
-This is the main method in the class, and it takes one argument (`target_column`) which is the target column in the dataset that we want to predict. The possible values are:
-- "MathScore"
-- "WritingScore"
-- "ReadingScore"
-
-When selecting one of these target columns, the column will be encoded categorically, and the model will be trained to predict the categories. The number of categories is set by `nb_bins`. By default, the number of bins (classes) is `5` which represents the following categories:
-- 0: (0 - 20]
-- 1: (20 - 40]
-- 2: (40 - 60]
-- 3: (60 - 80]
-- 4: (80 - 100]
-
-You can choose a higher number of bins which will have more bins with smaller score intervals. Or, choose a lower number of bins which will have fewer bins with larger score intervals.
-
-### Dataset
-- students-exam-scores
-- Kaggle link: https://www.kaggle.com/datasets/desalegngeb/students-exam-scores
-
-### CI/CD
-Provided a yaml file `.github/workflows/app.yml` configuring a build run before authorizing a commit. The workflow runs tests configured in `tests/test_student_framework.py` using pytest.

mlflow_data_versioning/README.md

@@ -0,0 +1,2 @@
+# MLOPs - MLflow mini project
+