You will get an Amazing stocks price prediction model with +90% accuracy
Project details
This is a Python code for training a Long Short-Term Memory (LSTM) neural network to predict the stock prices of Tesla, using historical data from the 'TSLA.csv' file. The code imports necessary libraries such as NumPy, Pandas, and Matplotlib, and also includes the 'StandardScaler' and 'MinMaxScaler' classes from the Scikit-learn library. It then reads the CSV file using Pandas and splits the data into training and testing sets.
The training data is preprocessed by removing the 'Date' and 'Adj Close' columns and scaling it using the 'MinMaxScaler'. The scaled data is then split into input (X_train) and output (y_train) sequences, each containing 60 days of historical data, which will be used to train the LSTM model. The model architecture consists of four LSTM layers, each followed by a dropout layer, and a final dense layer. The model is compiled using the 'adam' optimizer and 'mean_squared_error' loss function and trained for 20 epochs with a batch size of 32.
The testing data is preprocessed in a similar way, and input sequences of 60 days are generated. The trained LSTM model is used to predict the corresponding output sequences of stock prices.
The training data is preprocessed by removing the 'Date' and 'Adj Close' columns and scaling it using the 'MinMaxScaler'. The scaled data is then split into input (X_train) and output (y_train) sequences, each containing 60 days of historical data, which will be used to train the LSTM model. The model architecture consists of four LSTM layers, each followed by a dropout layer, and a final dense layer. The model is compiled using the 'adam' optimizer and 'mean_squared_error' loss function and trained for 20 epochs with a batch size of 32.
The testing data is preprocessed in a similar way, and input sequences of 60 days are generated. The trained LSTM model is used to predict the corresponding output sequences of stock prices.
Machine Learning Tools
Google Sheets, Keras, MATLAB, Microsoft Excel, NLTK, Open Neural Network Exchange, pandas, Python, Python Scikit-Learn, scikit-learn, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$60
|
Standard
$125
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 1 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 3 | 5 | 10 |
Number of Graphs/Charts | 5 | 10 | 0 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
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+$30 - $100
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+$30
Additional Model Variation
(+ 1 Day)
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+$50
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+$30Frequently asked questions
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ND
Nash D.
Jul 29, 2024
It is necessary to collect and structure the following data from open sources
IS
Illya S.
Apr 13, 2024
Machine Learning Expert Needed (23767)
Good freelancer, will hire again.
MJ
Mick J.
Nov 4, 2023
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Jun 15, 2023
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About Azeen
Data Scientist
Bishkek, Kyrgyzstan - 12:36 am local time
Steps for completing your project
After purchasing the project, send requirements so Azeen can start the project.
Delivery time starts when Azeen receives requirements from you.
Azeen works on your project following the steps below.
Revisions may occur after the delivery date.
Gather requirements:
Collect the necessary information from the client, including the company of interest, time frame, data availability, performance expectations, budget, and timeline.
Data collection and preparation
Obtain historical stock price data for the company of interest and prepare the data for analysis.