Valentius Kryptix

I’m excited to share my latest Machine Learning milestone, “Tuned Ensemble Pipeline,” completed as part of my internship at Valentius Kryptix.

The main objective of this milestone was to take a baseline Machine Learning model and improve it systematically using ensemble learning, hyperparameter tuning, and deliberate feature selection. Instead of relying on manual experimentation, the complete process was designed as a reproducible Machine Learning pipeline.

Project Overview

For this project, I worked with the Titanic dataset, a well-known dataset used for binary classification problems. The goal was to predict whether a passenger survived based on different passenger-related features.

The dataset contains information such as:

• Passenger Class
• Sex
• Age
• Number of Siblings/Spouses
• Number of Parents/Children
• Fare
• Port of Embarkation

The target variable was Survived, where the model predicts whether a passenger survived or not.

1. Building the Baseline Model

I started by creating a Decision Tree Classifier as the baseline model.

Before training the model, I performed basic data preprocessing, including handling missing values and converting categorical variables into numerical features using one-hot encoding.

The dataset was then divided into training and testing sets using a stratified train-test split. The baseline model was evaluated using multiple classification metrics, including:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

This baseline provided a reference point for measuring the improvement achieved through the later stages of the pipeline.

2. Applying Ensemble Learning

The next step was to improve the baseline using an ensemble method.

I implemented a Random Forest Classifier, which combines multiple decision trees and aggregates their predictions. Random Forest can generally provide better generalization than a single decision tree because it reduces the effect of individual-tree variance.

The Random Forest model was trained using the same training and testing data so that the comparison with the baseline remained fair.

3. Systematic Hyperparameter Tuning

After building the Random Forest model, I performed systematic hyperparameter optimization using GridSearchCV.

Instead of manually changing parameters and selecting a model based on trial and error, GridSearchCV automatically evaluated combinations of different parameter values using cross-validation.

The parameters considered included:

• n_estimators
• max_depth
• min_samples_split
• min_samples_leaf

A 5-fold cross-validation strategy was used with accuracy as the optimization metric.

This process helped identify a better-performing Random Forest configuration based on systematic evaluation rather than intuition.

4. Feature Selection

Another important part of this milestone was deliberate feature selection.

After training the Random Forest model, I analyzed feature importance values to understand which features contributed most to the model’s predictions.

Features with importance below the selected threshold were removed, while more influential features were retained for the final model.

This step helped reduce unnecessary information and made the final pipeline more focused on the features that contributed meaningfully to prediction.

Feature importance also provided useful interpretability into how different passenger characteristics influenced the model.

5. Final Tuned Ensemble Pipeline

The final pipeline combined the three major techniques:

Random Forest Ensemble → GridSearchCV Hyperparameter Tuning → Feature Selection

The final model was then evaluated using the same metrics used for the baseline model.

The results were compared across four stages:

1. Baseline Decision Tree
2. Random Forest Ensemble
3. Tuned Random Forest
4. Tuned Random Forest with Feature Selection

This comparison made it possible to measure the effect of each improvement instead of simply reporting the performance of the final model.

Key Learning

The most important learning from this milestone was that improving a Machine Learning model is not only about selecting a more complex algorithm.

A strong Machine Learning solution requires a systematic workflow involving:

Data Preprocessing → Baseline → Ensemble Learning → Hyperparameter Tuning → Feature Selection → Evaluation

Using GridSearchCV demonstrated how systematic experimentation can replace manual trial and error. Feature importance also showed how model performance and interpretability can be considered together.

Conclusion

The Tuned Ensemble Pipeline milestone gave me practical experience in building a complete and reproducible Machine Learning workflow.

By starting with a baseline Decision Tree and progressively applying Random Forest, hyperparameter tuning, and feature selection, I was able to analyze how each technique affects model performance.

This project strengthened my understanding of ensemble learning, model optimization, feature engineering, cross-validation, and performance evaluation.

The complete source code and project implementation are available on GitHub:

GitHub: https://github.com/Spratik2025/ML_C1_Tuned_Ensemble_Pipeline

I’m grateful to Valentius Kryptix for providing the opportunity to work on practical Machine Learning tasks and improve my understanding through hands-on implementation.

#DataScience #Internship #ValentiusKryptix #Python #MachineLearning #EnsembleLearning #RandomForest #HyperparameterTuning #GridSearchCV #FeatureSelection #ScikitLearn #GitHub