Future Funds
Oracle
This dataset is about the direct phone call marketing campaigns, which aim to promote term deposits among existing customers, by a Portuguese banking institution from May 2008 to November 2010
In visualization, we aim to uncover relationships between features and create graphs that help us better understand the dataset. These visual representations assist in simplifying complex patterns, making it easier to comprehend the data
A fixed deposit predictive system using different trained models is a crucial step to assess and compare the performance of various algorithms. Here is a generalized process for testing by different trained models
A Random Forest Classifier is an ensemble learning method that combines multiple decision trees to create a robust and accurate predictive model.Each decision tree is trained on a random subset of the data and a random subset of the features.
Accuracy of training data: 1.0 | Accuracy of test data: 0.91
Logistic Regression is a statistical method used for binary classification tasks. Logistic Regression is a valuable tool in predictive modeling, especially when dealing with binary classification problems and when interpretability of the model's predictions is crucial. Its simplicity, efficiency, and interpretability make it a popular choice in various fields.
Accuracy of training data: 0.91 | Accuracy of test data: 0.91
A decision tree is a tree-like model that is used to make decisions or predictions. It recursively splits the dataset into subsets based on the most significant attributes, creating a tree structure where each internal node represents a decision based on a specific feature, and each leaf node represents the final output or decision
Accuracy of training data: 0.93 | Accuracy of test data: 0.90After implementing and testing various modules, we are now prepared for predictions. We're using the module with the highest accuracy score, ensuring our predictions are reliable and accurate.
