The project focuses on building a real time placement prediction model from kaggle datasets. It consists of following components,
1. Raw_Placement_Data : The unprocessed dataset, extracted from kaggle. It consists of various data inconsistencies, including missing vlaues, abnormal values etc.
2. Code_For_Pre_Processing : Complete python code used for data processing, starting from importing raw data to saving final processed data. It also includes different data visualizations between variables.
3. processed_dataset : The filtered dataset derived after applying different data cleaning methods. All variables in this data are numerical.
4. model_selection_and_evaluation : The python code used to test different models relevant to the usecase and fitting the dataset upon the best one. It also includes model evaluation across different performance metrics.
5. placement_model.pkl : The final model build after training and testing on the processed dataset. The model is an Ensemble of a Decision Tree Classifier.
6. setting_Up_UI : The codebase for Setting up the User Interface for the predictive model.
The overall score of the model upon training data was 83 (out of 100). The UI of the model can be accessed by running the above code "setting_Up_UI" by calling the "placement_model.pkl" model