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","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None

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)

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.

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

Detection of Fake Accounts in Social-Media(Instagram) Using Machine Learning

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.

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.

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.

Used libraries and functions as follows:

Production-style fraud detection pipeline for financial transactions using behavioural analytics, anomaly detection, and machine learning with out-of-time validation and explainable risk scoring.

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

Exoplanet Hunting in Deep Space.

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

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.

Perform Dimensionality Reduction using AutoEncoder.

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.

Bank Beta Company focus on retain existing customers, our task is to create a model that predicts whether or not a customer will leave the bank soon.

A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.