Multi-Class-Classification-Text-Analysis-and-Time-Series-Forecasting-MLOps-CICD-

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Historical Weather Time Series Forecasting

High-level Description

This project aims to forecast historical weather time series data using machine learning regression techniques. It utilizes the Historical Weather API (Open-Meteo) to retrieve temperature data at 2m from the ground for the City of France. The primary objective is to develop accurate time series forecasting models through experimentation with different architectures such as statistical models and machine learning regression models.

Data Flow & Architecture

The data flow involves retrieving weather data from the Historical Weather API, preprocessing it, training machine learning models, evaluating model performance, and serving predictions through a FastAPI server. The architecture encompasses Python scripts for data retrieval, preprocessing, model training, evaluation, and serving, as well as Docker for containerization of the FastAPI server.

Main Technologies Used and for Which Purpose

Python: Main programming language for scripting, data manipulation, and model development.
Pandas, NumPy: Data manipulation and preprocessing.
Statsmodels, scikit-learn: Statistical models and machine learning regression techniques.
FastAPI: Web framework for serving machine learning models.
Docker: Containerization of the FastAPI server for deployment.
Running Locally

To run the project locally, follow these steps:

Install Dependencies

Ensure you have Python installed on your system.
Clone the repository.
Navigate to the project directory.
Install dependencies in requirement.txt make sure you have the right python venv that contains the sqlite dependencie
listing the dependencies in requirement.txt: pandas~=2.2.1 numpy~=1.26.4 fastjsonschema==2.19.0 uvicorn~=0.24.0.post1 fastapi~=0.104.1 pydantic~=2.5.2 scikit-learn>=0.24.0 openmeteo-requests~=1.2.0 requests-cache retry-requests statsmodels configparser
Run

Execute the train.py script to retrive data using the common class save it to db and train model and saves it as pickel in api/model/ folder (make sure you adjust the paths
for local run
Execute evaluate.py for model evaluation , new tables will be added to the db and to visualization/monitoring linear_reg_plot.png.
Execute the main.py the app will run on the port 8000 by adding /docs to the giving http url you can see swagger ui and test api endpoints (predict day 2024-04-01) always try for 5 days in past because api cant provide data till the current day you are in
Build docker image

on the terminal run docker build -t ts-prod .
the if build of the image is perfectly built run : docker run -d -p 8000:8000 ts-prod
you should get the app runing on same local port 8000 but in docker container
Test

To test the project locally (docker container), you can manually make HTTP requests to the FastAPI server endpoints using swagger ui.

CI/CD Steps

Step 1: Build and Run Docker Image

Description: This step builds a Docker image named ts-project from the repository code and pushes it to GitHub Packages.
Outputs:
If successful, the Docker image will be pushed to GitHub Packages under the repository you_username/time_series_mlops/ts-project.
Step 2: Run Automatic Tests

Description: This step pulls the Docker image from GitHub Packages, runs it as a container, executes automatic tests on the API, and prints the container logs before and after the test.
Outputs:
Container logs before curl request.
Result of the API test request (e.g., response code, response body).
Container logs after curl request.