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.

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

Titanic Machine Learning from Disaster

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

Build and Deploy a binary classification model as a plagiarism detector

The given data includes airline reviews from 2016 to 2019 for popular airlines around the world with multiple choice and free text questions. Data is scrapped in spring2019.The main objective is to predict whether passengers will refer the airline to their friends.

Telecom companies face significant revenue loss due to customer churn. This project uses data manipulation, visualization, and machine learning to build a predictive model that identifies at-risk customers.

This Project is about to identify if a person's back pain is abnormal or normal using collected physical spine details/data.

The aim of this project is to predict fraudulent credit card transactions using machine learning models.