You will get a machine learning model that automates stock prices forecasting.
Project details
Stock Prices Prediction Model using LSTM
Overview
This project aims to develop a stock prices prediction model using LSTM (Long
Short-Term Memory) neural networks. The model was trained on historical
stock prices data to predict the future trends of the stock prices.
Dataset
The dataset used for this project is the historical stock prices data of Google,
Microsoft, Facebook, Amazon, Tesla, and Apple, obtained from Yahoo Finance.
The dataset consists of daily stock prices data spanning the last 10 years.
Preprocessing
Before feeding the data to the LSTM model, some preprocessing steps were,
including: - Checking for and handling missing values - Normalizing the data -
Splitting the data into training and testing sets
LSTM Model Architecture
This is a two-layer LSTM model with one input layer, one hidden layer with 128
units, and one output layer. The input shape is defined as (x_train.shape[1],
1), where x_train is the training data, and the second parameter is the number
of features. The model will be trained on the preprocessed training data, and its performance
will be evaluated on the preprocessed testing data.
Evaluation Metrics
Root Mean Squared Error (RMSE).
Overview
This project aims to develop a stock prices prediction model using LSTM (Long
Short-Term Memory) neural networks. The model was trained on historical
stock prices data to predict the future trends of the stock prices.
Dataset
The dataset used for this project is the historical stock prices data of Google,
Microsoft, Facebook, Amazon, Tesla, and Apple, obtained from Yahoo Finance.
The dataset consists of daily stock prices data spanning the last 10 years.
Preprocessing
Before feeding the data to the LSTM model, some preprocessing steps were,
including: - Checking for and handling missing values - Normalizing the data -
Splitting the data into training and testing sets
LSTM Model Architecture
This is a two-layer LSTM model with one input layer, one hidden layer with 128
units, and one output layer. The input shape is defined as (x_train.shape[1],
1), where x_train is the training data, and the second parameter is the number
of features. The model will be trained on the preprocessed training data, and its performance
will be evaluated on the preprocessed testing data.
Evaluation Metrics
Root Mean Squared Error (RMSE).
Machine Learning Tools
Keras, NumPy, Python, Python Scikit-Learn, TensorFlowWhat's included
| Service Tiers |
Starter
$50
|
Standard
$60
|
Advanced
$70
|
|---|---|---|---|
| Delivery Time | 12 days | 10 days | 7 days |
Number of Revisions | 0 | 0 | 0 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Gloryson
Machine Learning Engineer
Nairobi, Kenya - 12:21 pm local time
Steps for completing your project
After purchasing the project, send requirements so Gloryson can start the project.
Delivery time starts when Gloryson receives requirements from you.
Gloryson works on your project following the steps below.
Revisions may occur after the delivery date.
Overview
This project aims to develop a stock price prediction model using LSTM (Long Short-Term Memory) neural networks. The model was trained on historical stock price data to predict the future trends of the stock prices.