Predicting Crude Oil Prices Using Deep Learning: A Python Tutorial
Download the source code from here:
https://onepagecode.substack.com/
This video provides a detailed walkthrough on predicting crude oil prices using deep learning techniques in Python. It begins with basic steps such as importing necessary libraries (numpy, pandas, matplotlib) and loading a CSV file into a DataFrame. The tutorial covers essential data preprocessing tasks like handling missing values, plotting data, and normalizing stock data for model readiness. It then delves into the construction of a Recurrent Neural Network (RNN) model, highlighting how to build, compile, and train the model for a regression task, with a focus on reducing learning rates and implementing callbacks for optimization. The process of making predictions, inverting scaling to interpret results, and plotting actual vs. predicted prices demonstrates the practical application of RNNs in financial forecasting. This guide is an invaluable resource for those looking to apply deep learning to forecast financial market trends, particularly in the crude oil sector.
00:00:00 Imports numpy, pandas, matplotlib, and io
00:00:44 Upload and display file information
00:01:42 Reads a CSV file into a DataFrame
00:02:27 Displays first 5 rows of dataframe
00:02:56 Prints the shape of the dataframe
00:03:27 Count missing values in each column
00:04:14 Forward fill missing values in dataframe
00:05:00 Plots close price vs time graph
00:06:49 Converts and reshapes closing stock data
00:07:35 Accesses first column of closing stock
00:08:29 Select and reshape closing stock data
00:09:32 Normalizes stock data between 0 and 1
00:10:25 Split data for training and testing
00:11:13 Prints out the train and test datasets
00:12:15 Slices and prints subset of a list
00:13:11 Extracts data for testing purposes
00:13:48 Reshapes train and test arrays
00:14:27 Prints shapes of train and test datasets
00:15:13 Process data into features and labels
00:16:15 Processes data for training and testing
00:17:37 Prints shapes of training and testing data
00:18:28 Reshapes arrays to include time dimension
00:19:03 Import libraries for deep learning model
00:20:45 Reduce learning rate and save model
00:22:09 Builds a recurrent neural network model
00:24:32 Compiles a model for regression task
00:25:15 Model training with callbacks and validation
00:26:00 Plots mean squared error and loss
00:27:21 Calculate scores for training and testing data
00:28:29 Make predictions and invert scaling
00:29:29 Reshapes and scales testY data
00:30:28 Plot actual and predicted prices over time
00:32:02 Slice last 10 elements from testY
00:32:33 Selects elements from 10th to last
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