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

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

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…

🧪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.

Exoplanet Hunting in Deep Space.

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.

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

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.

Used libraries and functions as follows:

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.

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

OilyGiant mining company finding the best place for 200 new well points, As an Data Scientist we're creating a model who can choose the best 200 point by profit and risk.

Telecom Customer Churn Prediction Using Machine Learning!

It is a Hackathon problem statement solution, which is arranged by Analytics Vidhya.