This project is aimed to determine the most suitable Regression model for a given dataset, with the goal of optimizing predictive accuracy. The methodology involved an exhaustive exploration of various regression models, including:
- Linear, 2. Decision Tree, 3. Random Forest, 4. Ridge, 5. Lasso, and 6. Support Vector Regression (SVR).
The dataset was carefully preprocessed and trained, and evaluation metrics:
- R-squared, 2. Mean Squared Error (MSE), 3. Root Mean Squared Error (RMSE), and 4. Mean Absolute Error (MAE)
were employed to quantitatively compare model performance.
Rigorous feature selection methods:
- Filter techniques - A. Feature Importance, B. Variance Threshold, C. SelectKBest using ANOVA F-value, D. SelectPercentile
- Wrapper Technique - A. Backward selection, B. RFE (Backward selection method), C. RFE with Cross-Validation (Method 1), D. RFE with Cross-Validation (Method 2)
were also utilised to find most important features.
Feature selection procedures also highlighted the importance of specific predictor variables in enhancing model accuracy. These results have significant implications for decision-making processes reliant on accurate predictions, spanning fields such as finance, economics, and engineering.
The study's outcomes underscore the critical role of model selection in achieving optimal predictions, enabling stakeholders to make informed choices based on robust data-driven insights. The methodologies and insights gleaned from this study provide a valuable framework for selecting regression models tailored to specific datasets, contributing to the advancement of accurate and reliable predictive modeling.
Regression model selection plays a pivotal role in data analysis by determining the most appropriate mathematical framework for modeling relationships between variables. It holds profound significance due to its potential to enhance the accuracy, interpretability, and generalization capabilities of predictive models.
The core essence of regression analysis lies in its ability to unveil hidden patterns, quantify associations, and make informed predictions. However, the effectiveness of these outcomes is directly linked to the selection of the right regression model. A misaligned choice can lead to biased estimates, unreliable predictions, and flawed insights.
A key aspect of regression model selection is striking a balance between model complexity and generalization. Overly complex models may overfit the training data, capturing noise and leading to poor performance on new data. On the other hand, overly simplistic models may overlook important relationships, resulting in underfitting.
Furthermore, different regression techniques cater to distinct types of relationships and data structures. For instance, linear regression assumes a linear relationship between variables, while advanced methods like ridge and lasso regression are designed to handle multicollinearity and prevent overfitting. The relevance of regression model selection extends across diverse fields. In economics, it aids in predicting market trends and consumer behavior. In healthcare, it supports disease prognosis and treatment effectiveness assessment. Environmental science benefits from modeling complex interactions. In all domains, informed model selection translates to more accurate insights, better decision-making, and enhanced problem-solving capabilities.
In conclusion, the careful selection of a regression model is a cornerstone of effective data analysis. It ensures the model's compatibility with the data's nature, enhances predictive accuracy, and amplifies the potential for extracting meaningful insights. As the volume and complexity of data continue to grow, the role of regression model selection becomes increasingly pivotal in unlocking the true value of data-driven exploration and decision-making.
I have created 2 Optimal Regression Model Selection Jupyter notebooks using CSV and Excel datasets.
Forest_fires_Regression is using CSV file and Energy_Mgmt_Regression is using Excel file.