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

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

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

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

A Python Machine Learning Project designed to predict Halloween Candy sales for a company based on historical data

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 process of computationally identifying and categorizing opinions expressed in a piece of text, especially to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Understanding people’s emotions is essential for businesses since customers are able to express their thoughts and fe…

Build and Deploy a binary classification model as a plagiarism detector

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

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

To import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. I will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.

The main purpose of this repository is to build the pipeline for training of regression models and predict the compressive strength of concrete to reduce the risk and cost involved in discarding the concrete structures when the concrete cube test fails.

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.

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

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

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📊📈📉👨‍💻

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

Label-Free Model Evaluation and Weighted Uncertainty Sample Selection for Domain Adaptive Instance Segmentation

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