Data Preparation: The first step is to clean and prepare the data. This might include handling missing values, encoding categorical variables, and scaling numerical variables.
Feature Engineering: This involves creating new features from the existing ones or transforming existing features to a more useful scale.
Model Training: Several algorithms can be used for this purpose. Decision Trees, Random Forest, SVM, and Logistic Regression are popular choices for binary classification problems.
Model Evaluation: After training, the model's performance must be evaluated using metrics like accuracy, precision, recall, and F1 score.
Interpreting the Model: Depending on the model used, various techniques can be applied to interpret the features affecting the prediction.
Deployment: Finally, the model is deployed into a production-like environment where it can take in new data and make predictions.
Technology: Python: The core programming language used for the implementation of the predictive model. Python is renowned for its ease-of-use and extensive libraries, making it the go-to language for data science tasks.
Pandas: A powerful Python library for data manipulation and analysis. It's used for preparing the dataset and feature engineering.
Scikit-learn: A widely-used Python library for machine learning. It's employed here to split the data into training and testing sets, create a Random Forest Classifier model, train the model, and evaluate its performance.
Random Forest Classifier: A machine learning algorithm that is part of the ensemble learning methods. It's used for classification tasks and is known for its high accuracy, ability to handle large data sets with higher dimensionality, and its ability to handle missing values.
Feature Engineering: The process of selecting and transforming variables when creating a predictive model. Feature engineering can significantly boost a model's predictive power.
Model Evaluation Metrics: Using metrics like accuracy, precision, recall, and F1 score for evaluating the performance of the machine learning model. These metrics give an in-depth understanding of how well the model is performing.
Interpretable Machine Learning: The project not only aims to make accurate predictions but also interprets those predictions using feature importances, thereby making the model more transparent.
Data Simulation: For demonstration purposes, synthetic data was used to train the model. This is particularly useful when you don't have access to real-world data.
Development Environment: The code can be developed and tested in Jupyter Notebooks, a popular tool among data scientists for creating and sharing documents that contain live code, equations, visualizations, and narrative text.