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bank-application-using-sagemaker-s3

bmw-car-price-prediction

boston-house-price-prediction

Flask Web Application

churn-prediction

Churn Modelling done using XGBoost, AdaBoost, GradientBoost

cltv-compass

The main objective of this project is to forecast the Customer Lifetime Value (CLTV) using user and policy data.

content-based-recommender-system

Movie Recommender system using basic NLP concepts - countvectorizer, cosine similarity. Web-application hosted on Streamlit.

customer-clusters-revealed

Who Are Your Real Customers? RFM Modelling & K-Means Reveal the Answer.

customer-payment-date-prediction

payment date prediction - regression statement

exoplanet-in-deepspace

DeepNet tuned with Kerastuner added with ensemble of LGBM, HistGBM

fake-news-classifier

This project contains 2 components. First component is to check whether the news is fake or not. Second component is of interactive graphs which are related to Covid-19.

financial-sentinel

The primary objective of this project is to detect fraud in a financial system as efficient as possible using AI.

future-forecasting-timeseries

ARIMA, SARIMA, AUTO-ARIMA

gans-sample_test

Testing GANs with CIFAR-10

geo-visualization

Geo-Visualization helps in visualizing various maps of the world on a different scale. This project is done to understand more about folium.

google-svhn-prediction-with-cnn-and-transfer-learning

Google SVHN Prediction using Keras Tuned CNN model and Transfer Learning

handwritten-digit-prediction-using-cnn

CNN on MNIST Data

mobile-classification-sagemaker

pca-with-breast-cancer-data

post-graduation-admission-prediction

Prediction using HistGBM and LightGBM

random

task-master

This is a simple 'Task Master' or a 'To-Do' web application. You can add, update or delete a task in this web-app. To view this project, click on - https://rajtaskmaster.herokuapp.com/

transfer-learning-functional-apis

Age and Gender prediction from facial images using VGG16 and the Keras Functional API.

unsupervised-customer-segmentation

Segmenting customers with Kmeans