A collection of machine learning projects developed as part of my learning and project work, covering data preprocessing, classification, model training, evaluation, and prediction.
A machine learning classification project that predicts whether a passenger survived the Titanic disaster. Models Used
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier Tools & Libraries
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
A machine learning classification project that predicts whether a customer is likely to churn. Models Used
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier
- XGBoost Tools & Libraries
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- XGBoost
- Data Cleaning & Preprocessing
- Exploratory Data Analysis
- Categorical Feature Encoding
- Feature Scaling
- Classification
- Model Training
- Model Evaluation
- Model Comparison
- Prediction Generation
- Python-based Machine Learning
Completed
These projects demonstrate my practical understanding of machine learning workflows and my ability to apply different classification algorithms to real-world datasets.