Linear Regression:
Use case: Predicting a continuous target variable based on one or more independent variables.
Strengths: Simplicity, interpretability, and good for modeling linear relationships.
Considerations: Assumes a linear relationship between variables, may not perform well with highly nonlinear data.
Random Forest:
Use case: Classification or regression tasks, feature selection, and handling missing data.
Strengths: High predictive accuracy, handles complex interactions, robust to outliers.
Considerations: May overfit with noisy data, can be computationally expensive for large datasets.
Gradient Boosting (e.g., XGBoost, LightGBM):
Use case: Both classification and regression problems, often used for structured/tabular data.
Strengths: Excellent predictive power, handles complex relationships, robust to outliers.
Considerations: Requires fine-tuning, may be prone to overfitting without regularization.
Neural Networks (Deep Learning):
Use case: Complex tasks like image recognition, natural language processing, and sequential data analysis.
Strengths: Can model highly complex and nonlinear relationships, state-of-the-art performance in many domains.
Considerations: Requires large amounts of data, computational resources, and expertise in model tuning.
Support Vector Machine (SVM):
Use case: Classification tasks, especially in situations with clear class separation.
Strengths: Effective in high-dimensional spaces, works well with limited data.
Considerations: May not perform well on large datasets, sensitive to parameter tuning.
Time Series Forecasting:
Prophet: Developed by Facebook for forecasting with daily observations and seasonal effects.
ARIMA: Suitable for univariate time series forecasting.
LSTM and GRU: Deep learning models for sequential data with long-term dependencies.
Time Series Models:
ARIMA (AutoRegressive Integrated Moving Average): Suitable for time series forecasting of variables like temperature and air quality.
Prophet: Developed by Facebook for forecasting with daily observations and seasonal effects.
LSTM and GRU: Deep learning models for sequential data with long-term dependencies.@
Anomaly Detection:
Isolation Forest: Detects outliers and anomalies in data.
One-Class SVM: Useful for one-class classification, where you want to identify data points that don't conform to the norm.
Linear Regression: Use case: Predicting a continuous target variable based on one or more independent variables. Strengths: Simplicity, interpretability, and good for modeling linear relationships. Considerations: Assumes a linear relationship between variables, may not perform well with highly nonlinear data.
Random Forest: Use case: Classification or regression tasks, feature selection, and handling missing data. Strengths: High predictive accuracy, handles complex interactions, robust to outliers. Considerations: May overfit with noisy data, can be computationally expensive for large datasets.
Gradient Boosting (e.g., XGBoost, LightGBM): Use case: Both classification and regression problems, often used for structured/tabular data. Strengths: Excellent predictive power, handles complex relationships, robust to outliers. Considerations: Requires fine-tuning, may be prone to overfitting without regularization.
Neural Networks (Deep Learning): Use case: Complex tasks like image recognition, natural language processing, and sequential data analysis. Strengths: Can model highly complex and nonlinear relationships, state-of-the-art performance in many domains. Considerations: Requires large amounts of data, computational resources, and expertise in model tuning.
Support Vector Machine (SVM): Use case: Classification tasks, especially in situations with clear class separation. Strengths: Effective in high-dimensional spaces, works well with limited data. Considerations: May not perform well on large datasets, sensitive to parameter tuning.
Time Series Forecasting: Prophet: Developed by Facebook for forecasting with daily observations and seasonal effects. ARIMA: Suitable for univariate time series forecasting. LSTM and GRU: Deep learning models for sequential data with long-term dependencies.
Time Series Models: ARIMA (AutoRegressive Integrated Moving Average): Suitable for time series forecasting of variables like temperature and air quality. Prophet: Developed by Facebook for forecasting with daily observations and seasonal effects. LSTM and GRU: Deep learning models for sequential data with long-term dependencies.@
Anomaly Detection: Isolation Forest: Detects outliers and anomalies in data. One-Class SVM: Useful for one-class classification, where you want to identify data points that don't conform to the norm.
Ensemble models: Combine different models