mohan-aditya05/forecasting-energy-consumption

Stars
1
Language
Jupyter Notebook
Created over 5 years ago
Updated over 5 years ago

mohan-aditya05/forecasting-energy-consumption

mohan-aditya05

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Code submitted for TCS HumAIn contest.

walmart_sales_forecasting

Using Time Series forecasting and analysis to predict Walmart Sales across 45 stores.

Jupyter Notebook

credit_card_customer_segmentation

Develop a customer segmentation to define market strategy. The sample dataset summarizes the usage behaviour of about 9000 active credit card holders during the last 6 months.

Jupyter Notebook

text_analysis_ocr_service

This repository contains the code for the OCR API service using the Flask backend.

telecom_churn_classification

The goal of this study is to apply analytical techniques to predict a customer churn and analyse the churning and non-churning customers by using data from an internet connection company.

Jupyter Notebook

text-clasification-toi

Text classification of Times of India articles on HIV-AIDS since 2010 using neural networks and web scraping using BeautifulSoup.

Jupyter Notebook

churn_modelling

Case Study for Churn Modelling in a NGO

Jupyter Notebook

image-captioning

Image captioning is the task of generating a caption for an image. We explore new models and analyse their performances. We will be using Tensorflow in this project.

Jupyter Notebook

credit-delinquency

A case study on whether a person would default on his bank loan or not. This case study makes predictions using three machine learning algorithms.

Jupyter Notebook

credit_card_spend_regression

Jupyter Notebook

cloudera-medicare-challenge

Jupyter Notebook

atm-gui

GUI implemented for ATM machine using JavaFX and JDBC.

competitive_programming

santander-customer-challenge

At Santander our mission is to help people and businesses prosper. We are always looking for ways to help our customers understand their financial health and identify which products and services might help them achieve their monetary goals. Our data science team is continually challenging our machine learning algorithms, working with the global data science community to make sure we can more accurately identify new ways to solve our most common challenge, binary classification problems such as: is a customer satisfied? Will a customer buy this product? Can a customer pay this loan? In this challenge, we invite Kagglers to help us identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted. The data provided for this competition has the same structure as the real data we have available to solve this problem.

Jupyter Notebook