Predicting lead conversion rates using a Logistic Regression model for an EdTech company.

This repository is a related to all about Deep Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

EDA, pre-processing, train & test models, select best-performing model based on accuracy, precision, recall, and F1-score

I build the Micrograd autogradient engine, which is a functioning neural network with forward pass, backward propagation, and stochastic gradient descent, all built from scratch. This is derived from the great @karpathy micrograd lecture. Each notebook is complete with Andrei's lecture code and speech, as well as my own code, anecdotes and addition

A Binary Classification application to predict if a car price is good deal or not based on a set of attributes using FastTree trainer based on the Multiple Additive Regression Trees (MART).

Malaria is a serious global health problem that affects millions of people each year. One of the challenges in diagnosing malaria is identifying infected cells from microscopic images of blood smears. Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have been used for image classification tasks etc

Binary classification using DeeplabV3plus on E-marg(custom) dataset that comprises of images and video analytics.

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Prediction Of Daibetes on the basis of following factors : - pregnancies , glucose , diastolic , triceps , insulin , bmi , dpf , age , diabetes

Insurance companies take risks over customers. Risk management is a very important aspect of the insurance industry. Insurers consider every quantifiable factor to develop profiles of high and low insurance risks. Insurers collect vast amounts of information about policyholders and analyze the data.

Spotify Music Classifier with Machine Learning Using Spotify API

Prediction of diabetes based on the signs and symptoms using machine learning algorithms

A Machine Learning Project for Event Classification into Signal or Background Noise

binary classification problem with tensor flow lite. very basic

Predicting Startup Acquisition Statuses using Machine Learning Pipelines!

Develop a technology for detecting mining sites using images from the optical satellite Sentinel-2. Specifically, it involves classifying images that contain mining sites and those that do not.

A classifier that can predict whether a passenger can survive the sinking of the Titanic

A model for binary classification of credit card data as fraudulent or legitimate

The model is trained on the dataset from kaggle. used CNNs for training model. Has a accuracy of 97%.