Uber stock prediction Model Using Phased LSTM | by Sunkanmi Temidayo | Feb, 2025

Uber Applied sciences, Inc. (ticker: uber) went public on Could 10, 2019, in one of the extremely anticipated IPOs of the last decade. Initially priced at $45 per share, Uber’s valuation reached roughly $82.4 billion. Nevertheless, the inventory confronted speedy challenges, closing its first buying and selling day at $41.57, reflecting investor considerations about profitability.

1. Pandemic and Diversification: The COVID-19 pandemic severely affected Uber’s core ride-hailing enterprise, resulting in a decline in income. Nevertheless, Uber Eats skilled exponential progress throughout this era, serving to offset losses and solidifying its position as a crucial income stream.
2. Path to Profitability: Uber achieved its first adjusted EBITDA-positive quarter in Q2 2021, signaling progress towards profitability. This milestone, mixed with cost-cutting measures and operational effectivity, bolstered investor confidence in its long-term progress potential.

This venture stands out to me as an enchanting problem after I found insights about Uber’s strategic journey throughout an interview with their CEO on YouTube. This piqued my curiosity, main me to conduct a collection of in-depth analysis efforts to raised perceive the elements driving Uber’s inventory efficiency. The mixture of exterior financial occasions, regulatory challenges, and inner diversification methods makes Uber’s story a novel case examine for predictive modeling. Tackling this venture permits me to mix my ardour for monetary evaluation and machine studying to uncover patterns in a real-world context.

The Uber Inventory Prediction Mannequin goals to investigate historic inventory worth knowledge and predict future worth developments utilizing machine studying strategies. The mannequin is constructed utilizing Python and in style knowledge science libraries.

• Load and discover Uber inventory worth knowledge.
• Carry out knowledge visualization to know developments.
• Prepare and consider a machine studying mannequin for inventory worth prediction.
• Generate predictions primarily based on historic patterns.

The dataset used on this venture contains:

• Uber’s historic inventory worth knowledge (Open, Shut, Excessive, Low, Quantity).
• Information sourced from Kaggle and loaded right into a Pandas DataFrame.

• The dataset is loaded utilizing Pandas.
• The ‘Date’ column is transformed to a datetime format.
• ‘Date’ convert to African/Lagos (GMT +1) to evolve to native time when predicting.
• Lacking values are checked and dealt with.

1. Time Sequence Plot: A line plot of the ‘Shut’ costs over time is generated to visualise the inventory worth development.
2. Seasonal Decomposition: The `seasonal_decompose` perform is used to investigate the time collection for seasonality, development, and residual parts.

1. LSTM Mannequin: An LSTM community is chosen for its potential to seize long-term dependencies in time collection knowledge.
2. 2. Mannequin Structure: The mannequin consists of two LSTM layers adopted by two Dense layers.
   — The primary LSTM layer has 128 items and returns sequences.
   — The second LSTM layer has 64 items and doesn’t return sequences.
   — The primary Dense layer has 25 items.
   — The ultimate Dense layer has 1 unit, representing the anticipated worth. 3.
3. Information Preparation: The information is break up into coaching and testing units. The coaching set is additional divided into enter sequences (X_train) and goal values (y_train).
4. Mannequin Compilation: The mannequin is compiled utilizing the ‘adam’ optimizer and ‘mean_squared_error’ loss perform.
5. Mannequin Coaching: The mannequin is skilled for 50 epochs with a batch measurement of 32.

1. Take a look at Information Preparation: The check knowledge is ready in an identical option to the coaching knowledge, creating enter sequences (X_test) and goal values (y_test).
   2. Mannequin Analysis: The mannequin is evaluated on the check knowledge utilizing the `consider` methodology, calculating the check loss.
   3. Prediction: The mannequin is used to foretell inventory costs for the check knowledge.
   4. Visualization: The precise and predicted costs are plotted collectively for comparability.
   5. Comparability Desk: A desk is created to show the precise and predicted costs side-by-side.

• The mannequin supplies a fundamental forecast of Uber’s inventory costs.
• Visualization helps determine developments and patterns in inventory worth motion.

The LSTM mannequin demonstrates the potential for predicting inventory costs. The outcomes present that the mannequin is ready to seize the overall development of the inventory worth motion. Nevertheless, additional enhancements will be explored by tuning hyperparameters, utilizing completely different architectures, or incorporating further options.

Challenge: Go to GitHub