📶Data Modelling

Predictive modeling involves using historical data to make predictions or forecasts about future outcomes. Here are three common types of predictive modeling techniques.

Classification: In classification predictive modeling, the goal is to categorize or classify data points into predefined classes or groups. The model learns from historical data where the classes are known, and it uses this knowledge to predict the class of new, unseen data. Common applications include spam email detection, sentiment analysis, and disease diagnosis.
Regression: Regression predictive modeling focuses on predicting a continuous outcome or variable. The model analyzes historical data to understand the relationships between input features and the target variable. It then uses this knowledge to make predictions about future, unseen data. Regression is commonly used in financial forecasting, sales predictions, and real estate price estimation.
Clustering: Clustering predictive modeling involves grouping similar data points together based on inherent patterns or similarities. The model identifies natural clusters within the data without predefined labels, making it an unsupervised learning technique. Clustering is useful in customer segmentation, anomaly detection, and organizing large datasets into meaningful groups.

What we offer:

Here's a breakdown of what PredictEasy offers after model building, particularly for both classification and regression tasks:

Summary Report (Classification and Regression):

Receive a comprehensive summary report that includes all key evaluation metrics for both classification and regression models.

Evaluation Metrics:

Metrics for Classification: Accuracy, F1 Score, Precision, Recall.
Metrics for Regression: Mean Absolute Error, Mean Squared Error, R-squared.

Explainable AI (XAI):
- Leverage Explainable AI to enhance model interpretability.
- Gain insights into how the model makes decisions, providing transparency and understanding.
ROC (Receiver Operating Characteristic) Curve:
- Visualize the performance of classification models with ROC curves.
- Understand the trade-off between sensitivity and specificity.
Confusion Matrix:
- Access the confusion matrix to analyze model performance in classification tasks.
- Understand true positives, true negatives, false positives, and false negatives.
Correlation Plots:
- Explore correlation plots to understand relationships between different variables.
- Visualize how variables interact with each other in the dataset.
Feature Rank:
- Access feature ranking to identify the most influential variables in the model.
- Understand which features contribute significantly to the model's predictive power.
Pairwise Grid:
- Utilize pairwise grid visualizations to analyze relationships between pairs of variables.
- Identify patterns and correlations in the data.

Real-Time Simulator:

With PredictEasy's real-time interface, where you have the opportunity to dynamically fine-tune model inputs and witness instantaneously the resulting outputs.
This immersive experience allows you to actively engage with your predictive model, enabling you to experiment with different scenarios and observe how changes in input variables directly influence predictions.
This helps you gain valuable insights into the reliability of these predictions by examining confidence levels associated with each output.
This dynamic and user-friendly interface empowers you to not only analyze predictions in real-time but also to understand the underlying factors that contribute to the model's decision-making process, enhancing your overall grasp of the predictive model's behavior.

Actionable Insights:

PredictEasy with Power AI goes beyond conventional model evaluation metrics, offering actionable insights and recommendations for enhanced decision-making.
Through interpretability features and detailed explanations, users can gain profound insights into the factors influencing model predictions.
The platform provides optimization tips and highlights feature importance, guiding users on fine-tuning models for improved performance. With continuous monitoring and collaborative decision-making features, PredictEasy ensures that users stay informed about model performance over time, enabling proactive adjustments and fostering a collaborative environment for data-driven decision-making.

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Last updated 1 year ago

What we offer:

Here's a breakdown of what PredictEasy offers after model building, particularly for both classification and regression tasks:

Summary Report (Classification and Regression):

Receive a comprehensive summary report that includes all key evaluation metrics for both classification and regression models.

Evaluation Metrics:

Metrics for Classification: Accuracy, F1 Score, Precision, Recall.

Metrics for Regression: Mean Absolute Error, Mean Squared Error, R-squared.

Explainable AI (XAI):

Leverage Explainable AI to enhance model interpretability.
Gain insights into how the model makes decisions, providing transparency and understanding.

ROC (Receiver Operating Characteristic) Curve:

Visualize the performance of classification models with ROC curves.
Understand the trade-off between sensitivity and specificity.

Confusion Matrix:

Access the confusion matrix to analyze model performance in classification tasks.
Understand true positives, true negatives, false positives, and false negatives.

Correlation Plots:

Explore correlation plots to understand relationships between different variables.
Visualize how variables interact with each other in the dataset.

Feature Rank:

Access feature ranking to identify the most influential variables in the model.
Understand which features contribute significantly to the model's predictive power.

Pairwise Grid:

Utilize pairwise grid visualizations to analyze relationships between pairs of variables.
Identify patterns and correlations in the data.

Real-Time Simulator:

With PredictEasy's real-time interface, where you have the opportunity to dynamically fine-tune model inputs and witness instantaneously the resulting outputs.

This immersive experience allows you to actively engage with your predictive model, enabling you to experiment with different scenarios and observe how changes in input variables directly influence predictions.

This helps you gain valuable insights into the reliability of these predictions by examining confidence levels associated with each output.

This dynamic and user-friendly interface empowers you to not only analyze predictions in real-time but also to understand the underlying factors that contribute to the model's decision-making process, enhancing your overall grasp of the predictive model's behavior.

Actionable Insights:

PredictEasy with Power AI goes beyond conventional model evaluation metrics, offering actionable insights and recommendations for enhanced decision-making.

Through interpretability features and detailed explanations, users can gain profound insights into the factors influencing model predictions.

The platform provides optimization tips and highlights feature importance, guiding users on fine-tuning models for improved performance. With continuous monitoring and collaborative decision-making features, PredictEasy ensures that users stay informed about model performance over time, enabling proactive adjustments and fostering a collaborative environment for data-driven decision-making.