PredEngine: Predictive modeling techniques for data-driven decision-making

This repository contains various predictive modeling techniques, such as regression, classification, clustering, time series, and anomaly detection for data-driven decision-making.

Predictive modeling is a technique for predicting future outcomes. It involves using data and statistical algorithms to predict future trends, behavior, and actions. This type of modeling can be applied in many fields, such as:

- Finance
- Insurance
- Marketing
- Healthcare
- Manufacturing
- etc. 

This repository contains the predictive modeling techniques and their applications.

Types of Predictive Models

1. Regression Models

   Examples:

   • Insurance Price Prediction Notebook

2. Classification Models

   Examples:

   • Loan Default Prediction Notebook
   • Customer Churn Prediction Notebook

3. Outlier/Anomaly Detection Models

   Examples:

   • Retail Store Sales outlier detection Notebook

4. Clustering Models

   Examples:

   • Mall Customer Segmentation Notebook
   • Banknote Authentication Notebook

5. Time Series Models

   I have two dedicated repositories for Time Series Classification and Time Series Forecasting:

   • Time Series Classification
   • Time Series Forecasting

Models

Various ML, DL, and statistical models are used in predictive modeling. The following table lists the models used for different types of predictive modeling:

Classification	Regression	Clustering	Time Series	Anomaly Detection
Logistic Regression	Linear Regression	K-Means	ARIMA	One-Class SVM
Decision Tree	Decision Tree	Hierarchical Clustering	Exponential Smoothing	Isolation Forest
Random Forest	Random Forest	DBSCAN	Croston's Method	Local Outlier Factor
Naive Bayes	Support Vector Machine	Mean Shift	LSTM	Autoencoder
Support Vector Machine	K-Nearest Neighbors	Gaussian Mixture Model	XGBoost
K-Nearest Neighbors	XGBoost
XGBoost

Requirements

requirements.yml file contains the list of all the packages required to run the code in this repository. requirements.yml is generated using the following command:

conda env export --no-builds | grep -v "prefix" > requirements.yml

To create a conda environment using the requirements.yml file, run the following command:

conda env create -f requirements.yml

Run inside Docker Container

To run the code inside a Docker container, follow the steps below:

1. Build the Docker image:

docker build -t pred_engine .

2. Run the Docker container:

docker run -it pred_engine

This will copy the repository inside the Docker container and install all the required packages.