The goal of this notebook is to implement and compare different approaches to predict item-level sales at different store locations.
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Updated
Jan 11, 2022 - Jupyter Notebook
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.
this is the 3 project of the deep learning nanodegree program of udacity
iPython notebooks for teaching and mishmash
Comparison of methods for predicting electricity consumption of a large non-residential building.
This repository consists of sample notebook which can take you through the basic deep learning excersises in Tensorflow and Pytorch
Time series prediction Notebooks
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%+.
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.
Contains notebooks of Time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA, Prophet model and LSTM model with forecast evaluation.
This notebook attempts to perform time-series forecasting using ARIMA and LSTM.
these are my projects that i submitted for AIML course with great lakes & some good notebooks with great explaination of the topics
Aplicación de modelos estadísticos al movimiento diario del Bitcoin con Python via Jupyter notebook.
This repository contains mini projects in machine learning in deep learing with notebook files
The following notebooks build and evaluate 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.
Market Trend analysis using LSTM Model. Use Jyputer Notebook .
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