Feature selection is essential for improving the accuracy and efficiency of machine learning models. It helps in identifying the most relevant features, eliminating noise, and reducing computational costs. In the article, I will focus on 4 main techniques. #DataScience #Python #MachineLearning #DataTalksClub #zoomcamp

The #AirQualityInMadrid project is complete! I’ve built and deployed a working air quality prediction system using time series models. Learned a lot through the process, from data wrangling to deployment. #MachineLearning #TimeSeries #DataScience #Python #DataTalksClub #zoomcamp #Machinelearning

After training and hyperparameter tuning, I’ve evaluated model performance and selected the best one. Deployed it with Flask & Docker for real-time air quality prediction! #MachineLearning #Deployment #Flask #Docker #Python #DataTalksClub #zoomcamp #Machinelearning

Using #Optuna for hyperparameter optimization to boost model accuracy on the #AirQualityInMadrid dataset. It’s all about finding the right parameters for better predictions! #MachineLearning #HPO #DataScience #Python #DataTalksClub #zoomcamp #Machinelearning

Now training models on the #AirQualityInMadrid dataset! Using LSTM, ARIMA, and other models with #Torch and #Darts. Excited to see how each model performs for time series forecasting. #MachineLearning #TimeSeries #Python #DataTalksClub #zoomcamp #Machinelearning

Data wrangling complete on the #AirQualityInMadrid dataset. Normalized the features, filled missing values, and reshaped the data for time series forecasting. Ready to train some models now! #DataScience #TimeSeries #MachineLearning #Python #DataTalksClub #zoomcamp #Machinelearning

Completing the EDA phase on the #AirQualityInMadrid dataset. Looking for patterns, handling missing values, and investigating the seasonal variations in air quality. EDA is crucial before we move on to model training. #TimeSeries #DataScience #Python #DataTalksClub #zoomcamp #Machinelearning