/Business-License-Multiclass-Classifier-Ensemble

Ensemble multi-class classifiers on business license status data.

Primary LanguageJupyter NotebookMIT LicenseMIT

Business license Multi-class Ensemble Classification

Aim

Implementing Ensemble techniques to predict license status for the given business.

Data Description

The dataset used is a licensed dataset. It contains information about 86K different businesses over various features. The target variable is the status of the license, which has five different categories.

Tech Stack

  • Language: Python
  • Libraries: pandas, scikit_learn, category_encoders, numpy, os, seaborn, matplotlib, hyperopt, xgboost

Approach

  1. Data Description
  2. Exploratory Data Analysis
  3. Data Cleaning
  • a. Missing Value imputation
  • b. Outlier Detection
  1. Data Imbalance
  2. Data Encoding
  3. Model Building
  • a. Random Forest
  • b. AdaBoost
  • c. XGBoost
  1. Feature importance
  2. Hyperparameter tuning
  • a. Random search optimization
  • b. Grid search optimization
  • c. Bayesian optimization

Modular code overview

  1. The input folder contains the data that we have for analysis. In our case, it contains Licence_Data.csv.

  2. The src folder is the heart of the project. This folder contains all the modularized code for all the above steps in a modularized manner.

    The model_selection.py and preprocessing.py files contain all the functions in a modularized manner, which are appropriately named. These Python functions are then called inside the run.py file.

    The requirements.txt file has all the required libraries with respective versions. Kindly install the file by using the command pip install -r requirements.txt.

  3. The output folder contains an Excel file for classification metrics scores of each model.

  4. The lib folder is a reference folder. It contains the original Jupyter notebook.