Predictive Modeling link

docs/_quarto.yml

@@ -257,6 +257,10 @@ website:
         text: "III. Financial Modeling" # "III. Advanced Financial Modeling in Python" # "III. Financial Modeling" #"III. Advanced Financial Modeling"
         contents:
 
+          - section:
+            href: notes/predictive-modeling/index.qmd
+            text: "Predictive Modeling"
+
           - section:
             href: notes/predictive-modeling/ml-foundations/index.qmd
             text: "Machine Learning Foundations"

docs/notes/predictive-modeling/index.qmd

@@ -5,24 +5,6 @@
 **Predictive modeling** refers to the use of statistical techniques and machine learning algorithms to predict future outcomes based on historical data. It involves creating models that learn patterns from past observations and use them to forecast future trends or behavior. At its core, predictive modeling is about understanding the relationships between variables and using these relationships to make informed predictions.
 
 
-
-
-## Predictive Modeling Process
-
-The process of predictive modeling can generally be broken down into several steps:
-
-  1. **Data Collection and Preparation**: Gather historical data and prepare it for analysis. This involves cleaning the data, handling missing values, and transforming features for better interpretability and accuracy.
-
-  2. **Model Selection**: Choose the right algorithm for the problem, whether it's a regression model, a classification algorithm, or a time-series forecasting model.
-
-  3. **Model Training**: Fit the model to the data by using training datasets to find patterns and relationships.
-
-  4. **Model Evaluation**: Validate the model to ensure that it generalizes well to new data. This typically involves splitting the data into training and testing sets or using cross-validation techniques.
-
-  5. **Prediction and Forecasting**: Once validated, the model can be used to predict outcomes on new or unseen data, providing valuable foresight for decision-making.
-
-In predictive modeling, the quality of the predictions is closely linked to the quality of the input data, the robustness of the algorithms, and the appropriate handling of uncertainties inherent in the real world.
-
 ## Relevance of Predictive Modeling in Finance
 
 Predictive modeling has become increasingly relevant in the financial industry due to its ability to analyze vast amounts of data and provide forecasts that support strategic decision-making. In finance, the ability to make accurate predictions can provide a significant competitive edge. Whether it's forecasting stock prices, assessing credit risk, or predicting customer behavior, predictive models have transformed how financial professionals make decisions.

docs/notes/predictive-modeling/ml-foundations/index.qmd

@@ -67,3 +67,19 @@ Machine learning problem formulation refers to the process of clearly defining t
   + Evaluation Metrics: Establishing how the model’s success will be measured. This could involve metrics like accuracy, precision, recall for classification problems, or mean squared error for regression problems.
 
 Proper problem formulation ensures that the right machine learning approach is chosen and that the model development process is aligned with the business or research objectives.
+
+
+
+## Predictive Modeling Process
+
+In practice, the process of predictive modeling can generally be broken down into several steps:
+
+  1. **Data Collection and Preparation**: Gather historical data and prepare it for analysis. This involves cleaning the data, handling missing values, and transforming features for better interpretability and accuracy.
+
+  2. **Model Selection**: Choose the right algorithm for the problem, whether it's a regression model, a classification algorithm, or a time-series forecasting model.
+
+  3. **Model Training**: Fit the model to the data by using training datasets to find patterns and relationships.
+
+  4. **Model Evaluation**: Validate the model to ensure that it generalizes well to new data. This typically involves splitting the data into training and testing sets or using cross-validation techniques.
+
+  5. **Prediction and Forecasting**: Once validated, the model can be used to predict outcomes on new or unseen data, providing valuable foresight for decision-making.