PROJECT 01
Insurance Cost Prediction — ML Web Application
An end-to-end machine learning project that predicts medical insurance costs using regression models. The
project includes data preprocessing, feature engineering, model comparison, hyperparameter tuning, and
Gradient Boosting for accurate predictions. The trained model is deployed as an interactive Streamlit web
application.
Python
Pandas
Scikit-learn
Gradient Boosting
Streamlit
Joblib
PROJECT 02
Customer Churn Predicition
An end-to-end machine learning project that predicts medical insurance costs using regression models. The
project includes data preprocessing, feature engineering, model comparison, hyperparameter tuning, and
Gradient Boosting for accurate predictions. The trained model is deployed as an interactive Streamlit web
application.
Python
Pandas
NumPy
Scikit-learn
Machine Learning
Classification
MLflow
Streamlit
PROJECT 03
Customer Segmentation
A clustering project using customer data to discover meaningful
groups and understand customer behavior through unsupervised
learning.
Python
Pandas
K-Means
Data Analysis
PROJECT 04
Machine Learning Experiments
A collection of experiments covering Logistic Regression, KNN,
SVM, Random Forest, XGBoost, PCA, model evaluation and
hyperparameter tuning.
Scikit-learn
XGBoost
PCA
ML