Bike Sharing Demand Prediction Project
تفاصيل العمل
Bike Sharing Demand Prediction Project: Key Insights ️
1. Dataset Overview:
Purpose: Predict hourly bike rental demand based on weather, time, and other features.
Features: Includes temperature, humidity, wind speed, season, hour, and more.
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Target: Continuous variable demand (total bike rentals).
2. Exploratory Data Analysis (EDA):
Scatter Plots: Highlighted relationships between demand and continuous variables like temperature and humidity.
Bar Charts: Explored categorical features (season, hour, holiday) vs. demand.
Outliers: Investigated demand distribution using quantiles for potential anomalies.
Correlation Matrix: Showed strong positive correlation between demand and temperature.
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3. Data Preprocessing:
Removed irrelevant columns (instant, dteday, casual, registered).
Renamed columns for clarity (e.g., cnt → demand, hum → humidity).
Converted categorical variables (e.g., season, hour) to dummy variables for modeling.
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4. Feature Engineering:
Log Transformation: Normalized demand to handle skewness.
Lag Features: Created t_1, t_2, and t_3 to address autocorrelation in demand.
? 5. Model Building:
Algorithm: Multiple Linear Regression was used for demand prediction.
Train-Test Split: 70% training, 30% testing.
Model Performance:
R² (Train): 88.27%
R² (Test): 85.93%
RMSE: 82.68
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6. Visualization Insights:
Predicted vs Actual: Strong alignment with minimal residuals.
Residual Analysis: Revealed a normal distribution and no patterns, validating model assumptions.
Learning Curve: Demonstrated reduced errors with increased training data.
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️ 7. Challenges Addressed:
Managed outliers in demand.
Addressed class imbalance in categorical variables with dummy encoding.
Handled autocorrelation with lag features.
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8. Future Enhancements:
Experiment with advanced models like Random Forest or Gradient Boosting.
Use GridSearchCV for hyperparameter tuning.
Engineer more complex features to capture seasonality and trends.
This project showcases how data preprocessing, feature engineering, and regression modeling can effectively predict bike-sharing demand, supporting urban mobility decisions!