Python developer to assist in building AI tool
Budget $250-750 USD
Job Description:
PLEASE MAKE SURE YOU READ THE ENTIRE DESCRIPTION BEFORE YOU PLACE BID
Hi,
I want to create my own AI tool and am looking for a developer who is an expert in:
Python, Numpy, Pandas and Tensorflow or other equally good for the project.
Are you capable of this and do you have knowledge of AI coding and technology, then please send me a quote of what this will cost.
This is for trading/stock/crypto prices , so it has to be able to use machine learning ie. Random Forest, Gradient Boosting, Support Vector Machines, LSTM, XGBoost, LightGBM, DBN, SGD, and especially RNN (neural networks) etc.
We will use the API from Alpaca. Need to make the trading bot created and fun from a server with a cron job. More details on this later.
The code below should not be foreign to you and you should be able to work with it:
import numpy as np
import pandas as pd
from [login to view URL] import MinMaxScaler
from [login to view URL] import Sequential
from [login to view URL] import Dense
# Load the stock data into a pandas dataframe
df = pd.read_csv('[login to view URL]') //or crypto data
# Scale the data using MinMaxScaler
scaler = MinMaxScaler()
df = scaler.fit_transform(df)
# Split the data into training and testing sets
train_data = df[:int([login to view URL][0] * 0.8), :]
test_data = df[int([login to view URL][0] * 0.8):, :]
# Create the input and output arrays for the training data
X_train = train_data[:, 1:]
y_train = train_data[:, 0]
# Create the input and output arrays for the testing data
X_test = test_data[:, 1:]
y_test = test_data[:, 0]
# Define the model
model = Sequential()
[login to view URL](Dense(12, input_dim=[login to view URL][1], activation='relu'))
[login to view URL](Dense(1, activation='linear'))
# Compile the model
[login to view URL](loss='mean_squared_error', optimizer='adam')
# Train the model
[login to view URL](X_train, y_train, epochs=100, batch_size=32)
# Evaluate the model on the test data
test_error = [login to view URL](X_test, y_test, verbose=0)
print('Test MSE:', test_error)
# Use the model to make predictions on new data
predictions = [login to view URL](X_test)
37 freelances font une offre moyenne de 606 $ pour ce travail
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