Feature Engineering: Analyze and preprocess your input features to e*xtract more meaningful information. This can involve techniques such as **scaling, normalization, handling missing values, or **creating new features* based on domain knowledge.
Model Selection: Experiment with different regression models to find one that better captures the relationship between the input features and the target variable. Consider trying different models such as Random Forest, Gradient Boosting, or Neural Networks.
Hyperparameter Tuning: Optimize the hyperparameters of your chosen model to find the best combination for your specific dataset. Use techniques like grid search or randomized search to explore different parameter settings.
Cross-Validation: Perform cross-validation to get a more reliable estimate of your model's performance. This can help identify overfitting or underfitting issues and guide further improvements.
Increase Training Data: If feasible, collect more training data to provide a larger and more diverse dataset for model training. More data can help improve the generalization ability of the model.
Feature Selection: Analyze the relevance of your input features and consider removing or adding features based on their impact on the model's performance. Use techniques like backward elimination or feature importance to guide your selection process.
Regularization: Apply regularization techniques like L1 or L2 regularization to reduce overfitting and improve the model's ability to generalize to new data.
Ensemble Methods: Consider using ensemble methods like bagging **or **boosting **to **combine multiple models and leverage their collective predictive power.
Remember that improving model performance is an iterative process, and it may require multiple iterations and experimentation to achieve the desired results.
What is feature engineering
What are the technique to extract meaningfull information
On what basis select model
hyperparameter tunning is best combination of-----------
cross validation identify the issues of ----------
On what basis feature selection does happens
regulization reduce ----
why use wnsamble method---------
purpose of bagging and boosting----------