AlmaBetter Project - AlmaBetter School
The Bike-Sharing Demand Prediction project aims to enhance the mobility and convenience of the public through bike-sharing programs in metropolitan areas. The goal is to predict the number of bikes required at any given time to ensure a consistent supply for rental. This project demonstrates my expertise in data analysis, machine learning, and predictive modeling.
-
Exploratory Data Analysis (EDA): Conducted comprehensive analysis of the dataset, exploring its structure, data types, and statistical summaries. Identified key features and patterns that impact bike demand, such as time, temperature, and weather conditions.
-
Data Cleaning and Preprocessing: Cleaned the dataset by handling duplicate values, missing data, and outliers. Ensured the integrity and quality of the data for accurate modeling.
-
Feature Engineering: Engineered relevant features and transformed variables to capture meaningful information for prediction. Performed feature encoding, handled multicollinearity, and applied transformations to improve model performance.
-
Model Training and Evaluation: Trained and evaluated various regression models using a scaled feature set. Employed techniques such as linear regression, regularization (Lasso, Ridge, Elastic Net), K-nearest neighbors, support vector machines, decision trees, random forests, boosting algorithms (AdaBoost, XGBoost, LightGBM), and hyperparameter tuning to optimize model performance.
-
Performance Metrics: Evaluated the models using industry-standard metrics such as R-squared, adjusted R-squared, and mean squared error (MSE). Selected the final model based on its performance and ability to make accurate predictions.
Through this project, I successfully developed a regression model to predict bike demand in metropolitan areas. The model achieved high accuracy and reliability in forecasting the number of bikes required at different time intervals.
The analysis revealed significant insights into the factors influencing bike demand, such as peak rental hours, temperature, seasonality, weather conditions, and humidity. These findings can assist in resource planning, optimizing bike allocation, and improving user experience in bike-sharing programs.
The project's structure, well-documented code, and comprehensive analysis demonstrate my skills in data analysis, machine learning, and project organization. I am confident that this project will attract recruiters and showcase my abilities in solving real-world problems through data-driven approaches.