Mastering Delivery Time Predictions: Insights Beyond AlgorithmsMastering Delivery Time Predictions: Insights Beyond Algorithms
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I built a Food Delivery Time Prediction project, and the most important thing I learned wasn't about the model.
It was about the data.
Before building any prediction model, I focused on understanding the operational patterns behind delivery performance and identifying the factors that actually influence delivery time.
Here’s what the workflow involved:
Dataset: 1,000 food delivery records across variables such as distance, weather, traffic level, vehicle type, courier experience, preparation time, and delivery duration.
Exploratory Data Analysis & Preprocessing: • Handled missing values across Weather, Traffic_Level, and Time_of_Day • Identified distribution patterns and operational trends within delivery timings • Analyzed the impact of variables like traffic conditions, distance, and preparation time on delivery performance • Performed data cleaning, transformation, and feature preparation for modeling
Models & Optimization: • Built baseline and predictive models using Linear Regression and Random Forest Regressor • Applied Hyperparameter Tuning using GridSearchCV with 5-fold cross-validation • Optimized model performance using R² score evaluation
Key Takeaway: Clean exploratory analysis and strong data understanding often contribute more to model performance than the algorithm itself.
Tools & Technologies: Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn
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