Clustering validation with ROC Curves

Lead generation for credit card

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

credit card lead prediction

Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","st…

Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.

🧪Predicting Loan Approvals 🚀Hill Climbing 🧮Ensemble Techniques

Data Science Project (Logistic Regression M7)

Increased the ROC AUC score by 2.14% of predicting the churn of users in telecommunication company using hypertuning parameter and feature engineering.

The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.

Developed a machine learning pipeline to detect fraudulent credit card transactions using Decision Tree and SVM classifiers. Applied preprocessing to handle imbalanced data and evaluated with ROC-AUC, achieving up to 0.96. Showcased skills in ML, data wrangling, and model evaluation.

Exoplanet Hunting in Deep Space.

Predicting the success of bank marketing campaigns using machine learning models (Random Forest, XGBoost) on customer and economic data. The project includes data preprocessing, model training, and evaluation with accuracy and ROC-AUC scores.

Beta Bank is losing customers monthly. Employees want to focus on client retention. As a Data Scientist, I created a model to predict the chance of a customer leaving, based on past behavior and contract terminations.

The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.

A Kaggle competition project predicting customer responses to insurance offers using XGBoost, focusing on feature engineering, visualization, and robust evaluation metrics.

ROC, AUC, and Z-score functions for anomaly detection

Used libraries and functions as follows:

Perform Dimensionality Reduction using AutoEncoder.

Telecom Customer Churn Prediction Using Machine Learning!