Predicting London’s climate using machine learning techniques. This project aims to forecast mean temperature in Celsius (°C) using various regression models and logging experiments with MLflow
You can interact with the model here: Climate Model
This project focuses on predicting the climate in London, England, specifically targeting the mean temperature in degrees Celsius (°C). With the increasing importance of weather predictions for businesses in the face of climate change, this project aims to develop a machine learning pipeline using various regression models.
The dataset used for this project is stored in london_weather.csv and includes the following columns:
date: Recorded date of measurement (int)cloud_cover: Cloud cover measurement in oktas (float)sunshine: Sunshine measurement in hours (hrs) (float)global_radiation: Irradiance measurement in Watt per square meter (W/m2) (float)max_temp: Maximum temperature recorded in degrees Celsius (°C) (float)mean_temp: Target mean temperature in degrees Celsius (°C) (float)min_temp: Minimum temperature recorded in degrees Celsius (°C) (float)precipitation: Precipitation measurement in millimeters (mm) (float)pressure: Pressure measurement in Pascals (Pa) (float)snow_depth: Snow depth measurement in centimeters (cm) (float)The preprocessing pipeline integrates the handling of missing values and data normalisation. This is achieved through the pipeline for SimpleImputer(replacing the missing values with mean values of each column) and StandardScaler respectively, ensuring seamless data preprocessing before model training.
Metric- Root Mean Square Error (RMSE)
After experimenting with various regression models:
RMSE of 0.861.Version 3 of the Random Regressor Model was moved to Production stage.
The Production Model achieved an accuracy of 97.7%.