The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.

In this Jupyter Notebook, I've used LSTM RNN with Technical Indicators namely Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Bollinger Bands to predict the price of Bank Nifty.

A set of notebooks that explores the power of Recurrent Neural Networks (RNNs), with a focus on LSTM, BiLSTM, seq2seq, and Attention.

A click bait classifier notebook developed using LSTM. The notebook showcases the analysis on Click bait heading data and a neural network to classify Heading as click bait. The model accuracy is 96%+.

Contains notebooks of Time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA, Prophet model and LSTM model with forecast evaluation.

Comparison of methods for predicting electricity consumption of a large non-residential building.

Statistical Models Application to Bitcoin Daily Movement with Python via Jupyter notebook.

Project made in Jupyter Notebook with "News Headlines Dataset For Sarcasm Detection" from Kaggle.

these are my projects that i submitted for AIML course with great lakes & some good notebooks with great explaination of the topics

this is the 3 project of the deep learning nanodegree program of udacity

Time series prediction Notebooks

This notebook attempts to perform time-series forecasting using ARIMA and LSTM.

This repository contains mini projects in machine learning in deep learing with notebook files

This is an update to run the deeplearning.ai notebook under music21 = 6.3.0 in your local machine.

A ML Notebook to forecast the electrical consumption of the city Haemstead using time series analysis,recurrent Neural Network(RNN).

Created as a demonstration to showcase the usefulness of RNN's with LSTM, in the pursuit of accurate stock price prediction models. Creating using Tensorflow. Written in Python-3 using Jupyter Notebook

This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the GOOGLE. Index by reiterating over the past 60 day stock price

This notebook builds an artificial recurrent neural network called Long Short Term Memory (LSTM) to predict the adjusted closing price of the NASDAQ Clean Edge Green Energy index by reiterating over the past 60 day stock price.

Python Jupyter notebooks for building and evaluating deep learning models using both the FNG values and simple closing prices to determine if the FNG indicator provides a better signal for cryptocurrencies than the normal closing price data.

Text prediction is a task that we use so often in our lives that we've taken it for granted. From the auto-fill feature in our messaging apps to search engines predicting search terms, text prediction technology saves our time and helps us to make our lives easier. It also links into other tasks such as text generation, which can eventually be u…