Python developer to assist in building AI tool

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 sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense

# Load the stock data into a pandas dataframe
df = pd.read_csv('stock_data.csv') //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(df.shape[0] * 0.8), :]
test_data = df[int(df.shape[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()
model.add(Dense(12, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(1, activation='linear'))

# Compile the model
model.compile(loss='mean_squared_error', optimizer='adam')

# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=32)

# Evaluate the model on the test data
test_error = model.evaluate(X_test, y_test, verbose=0)
print('Test MSE:', test_error)

# Use the model to make predictions on new data
predictions = model.predict(X_test)