Create a binary classifier that is capable of predicting whether applicants will be successful if funded by Alphabet Soup

People Counter App at the Edge

The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures

Benchmarking bank data to enhance marketing strategies. Models: Decision Tree and Random Forest. Libraries: Pandas, Matplotlib, Seaborn, Scikit-Learn, Numpy. Findings: Customer patterns and seasonal behaviors.

This repository contains a generalized regression analysis problem solved from scratch, using only the Numpy library.

TOP13% solution for the Titanic-Kaggle competition using a Gradient Boosting Classifier. Moreover, implementation of a Streamlit App to play with the models.

Learn linear quantization techniques using the Quanto library and downcasting methods with the Transformers library to compress and optimize generative AI models effectively.

Successfully established a machine learning model which can predict whether any given customer currently utilizing the products and services offered by a company will churn at anytime in the future or not, depending upon a set of unique features/characteristics pertaining to that specific individual, to a great level of accuracy.

Model Optimization using Batch Normalization and Dropout Techniques

Nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. Using machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded.

This is an End to End project designed to model a solution on Spain electricity shortfall challenges and make future prediction.

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Analyzed customer churn using transaction data. Built ML model to predict lapses. Dataset includes customer status, collection/redemption info, and program tenure. Delivered business presentation outlining modeling approach, findings, and churn reduction strategies.

Machine Learning: model optimization through hyperparameters

Neural network model implemented with flask and SQL to predict the success status of over 100,000 kickstarter companies.

Practice model assessment and optimization on the HR dataset using validation and dimensionality reduction techniques

ai-zipper offers numerous AI model compression methods, also it is easy to embed into your own source code

Develop a tool in Google Colab using machine learning and neural networks to select applicants for funding with the best chance of success based on the source data provided by the organization.

This is an End to End project and Api deployment for Spain electricity shortfall prediction