Key Components

1. Data Acquisition and Preprocessing

• Data Source: The dataset consists of daily bike-sharing trip records.

• Data Ingestion: Utilized Apache Hadoop and PySpark to handle large datasets efficiently. CSV files were loaded into a Spark DataFrame and saved on the Hadoop Distributed File System (HDFS).

• Data Cleaning and Transformation: The data was cleaned and transformed using PySpark, including converting timestamps to dates and aggregating trip counts by day.

2. Exploratory Data Analysis (EDA)

• Descriptive Statistics: Analyzed the central tendency, variability, and range of daily trips.

• Distribution Analysis: Examined the distribution and skewness of total trips per day.

• Visualization: Created histograms, box plots, and scatter plots to identify patterns and outliers.

• Seasonality and Trend Analysis: Decomposed the time series data to extract trend, seasonality, and residual components.

3. Model Development

Three models were developed and evaluated:

• Simple LSTM: A basic Long Short-Term Memory (LSTM) model with a single LSTM layer.

• LSTM with Hyperparameters: An enhanced LSTM model with multiple layers, dropout regularization, and hyperparameter tuning.

• GRU Model: A Gated Recurrent Unit (GRU) model with a similar architecture to the enhanced LSTM.

4. Model Training and Evaluation

• Training: Models were trained using TensorFlow, with a look-back period of 1 day for input sequences.

• Evaluation Metrics: Root Mean Squared Error (RMSE) was used to evaluate model performance on training and test datasets.

• Results: The GRU model achieved the best performance with the lowest RMSE on the test set, indicating its robustness in capturing temporal dependencies.

Insights and Conclusions

• Seasonal Patterns: The data exhibited clear seasonal patterns, with higher ridership in warmer months and weekdays.

• Model Performance: While the GRU model performed slightly better, all models showed limitations in prediction accuracy, suggesting that additional features (e.g., weather data, holidays) could improve performance.

• Future Work: Incorporating more contextual data and experimenting with different model architectures and hyperparameters could further enhance prediction accuracy.

Visualizations

• Time Series Decomposition: Visualized the trend, seasonality, and residual components of the time series data.

• Model Predictions: Plotted the actual vs. predicted trip counts for both training and test sets to illustrate model performance.

Technologies Used

• Data Processing: Apache Hadoop, PySpark

• Modeling: TensorFlow, Keras

• Visualization: Matplotlib, Seaborn

• Programming Languages: Python

This project demonstrates the potential of RNNs for time series forecasting in the context of bike-sharing systems, highlighting both the strengths and areas for improvement in predictive modeling.