Binary classification and Multiclass classification with pipelining and parameter tuning with GridsearchCV and RandomizedSearchCV

Building a model to predict demand of shared bikes. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels.

I developed the model to attain the predictive analysis in this task.

I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

This is a machine learning model built in python3 to predict transaction conversion of web visits for an e-commerce website.

This repository serves as a comprehensive resource for understanding and implementing various feature selection techniques, gaining familiarity with Jupyter Notebook, and mastering the process of model training and evaluation

A web application that employs machine learning models to provide accurate and instant car price estimations based on various features and specifications.

Training a model to predict whether a given job posting is fake or not

Data Preprocessing, Data Cleaning, Fine-tuning the Hyperparameters,

BC4AI：Blockchain Used to Guarantee Credibility of AI Model Evaluations;利用区块链来保证算法模型的真实性

Titanic Machine Learning from Disaster

A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it.

Model Evaluation is the process through which we quantify the quality of a system’s predictions. To do this, we measure the newly trained model performance on a new and independent dataset. This model will compare labeled data with it’s own predictions.

The tasks I was required to complete as a part of the BCG Open-Access Data Science & Advanced Analytics Virtual Experience Program are all contained in this repository. This virtual internship was sponsored by Forage📊📈📉👨‍💻

Welcome to the Loan Approval Prediction project repository! This project focuses on predicting the approval of loan applications using various machine learning algorithms. By analysing applicant details and financial information, the model aims to assist financial institutions in making data-driven and reliable loan approval decisions.

The aim of this project is to solve a Supervised Image Classification problem of classifying the flower types - rose, daisy, dandelion, sunflower, & tulip which can predict the class of the flower using the Convolutional Neural Networks (CNN), ResNet50 and transfer learning

Predicting the age of crabs using machine learning techniques based on physical characteristics.

Developed advanced regression models to predict house prices using the Ames Housing dataset. Achieved a grade of 90% under Prof. Vered Aharonson and ranked 550th in the Kaggle competition.

Towards evaluation of fairness in MDD models: Automatic analysis of symptom differences for gender groups in the D-vlog dataset