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This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.
YOLOv8 image classifier model comparison
Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.
Testing out ClearML.
Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
Repository to store materials for our group project on predicting health insurance charges.
Here I analysed data and made pipeline out of it making model making/training/testing/selecting and hyperparameters selecting more user friendly and visualised, like an application. I have worked for this project ~2 weeks.
I worked through these machine learning algorithms to complete assignments in the European Center for Medium Range Weather Forecasting MOOC class
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.
Predict the 10-year risk of coronary heart disease (CHD) using patient data from the Framingham Heart Study. The dataset contains over 4,240 records with 15 attributes, allowing for exploration, feature engineering, and the development of classification models
this project was inspired by Aurélien Géron's Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, where he performs detailed analysis on the Housing dataset. Motivated by that, I explored and applied similar machine learning techniques on the Student Habits and Academic Performance dataset to predict exam scores.
Tensorflow image classifier Keras Applications model comparison
In the 10-day camp, we experienced the basics of machine learning by coding
A machine learning foundation project to predict defaulting credit card clients. Topics covered are EDA, feature engineering and selection, model evaluation.
Graduate Rotational Internship Program -TSF-The Spark Foundation(Data Science and Business Analysis Internship) #GRIPJULY21-Task#2:Prediction Using Unsupervised Machine Learning-In this task, we have to predict the optimum number of clusters from the iris dataset & represent it visually.
Machine Learning project predicting heart disease risk using Logistic Regression & Random Forest (UCI dataset). Achieved 89% accuracy & 0.92 ROC-AUC, deployed as an interactive Streamlit web app.
Predicting customer subscriptions for a bank's term deposit post marketing campaigns
This repository explores machine learning models applied to predict online shoppers' purchase intentions based on a comprehensive dataset, showcasing various classification algorithms and their performance in the e-commerce domain.
This is an end to end machine learning project using my personal shopping data collected over the past three years.
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