MACHINE LEARNING PROJECTS
Summary
The dataset analyzed contains 768 observations of 10 numerical variables related to building characteristics, with heating load (mean 22.31 kWh, SD 10.09) and cooling load (mean 24.59 kWh, SD 9.51) as target variables. Key features include relative compactness (mean 0.76), surface area (mean 671.71 m²), and roof area (mean 176.60 m²), with roof area and overall height showing strong correlations (r=0.80) to energy loads. Data preprocessing revealed no missing values, outliers, or near-zero variance features, though linear dependencies led to removal of one redundant variable.
Two primary models were evaluated: Random Forest (RF) and Gradient Boosting Machines (GBM). For heating load prediction, RF achieved optimal performance with mtry=7 (RMSE 0.71, R² 0.994, MAE 0.45), while GBM with 900 trees, depth=3, and 0.1 shrinkage yielded RMSE 0.51 and MAE 0.37. For cooling load, RF with mtry=4 performed best (RMSE 1.93, R² 0.962, MAE 1.22), versus GBM with 500 trees and depth=7 showing RMSE 1.63 and MAE 1.04. The blended ensemble model achieved RMSE 0.78 for heating and 2.03 for cooling loads, with GBM marginally outperforming RF overall. Final model performance aligned with the reference study's MAE of 1.5, demonstrating effective prediction of energy efficiency metrics from architectural parameters.
Summary
The car price prediction project on Davidmac Olisa’s GitHub repository represents a robust application of data science techniques to solve a real-world problem. The project's primary objective is to develop a predictive model capable of estimating car prices based on key input features such as make, model, year, mileage, and additional attributes. This focus makes it a practical example of leveraging data for decision-making in the automotive industry.
The approach begins with Exploratory Data Analysis (EDA) to understand the dataset's structure and uncover relationships between variables. This step likely includes visualizations and summary statistics to identify trends, outliers, and important predictors of car prices. After EDA, the project involves feature engineering, refining the dataset by transforming variables or creating new ones to improve model performance.
The project employs advanced machine learning techniques, potentially including regression models like linear regression, decision trees, and ensemble methods such as Random Forest or Gradient Boosting. These algorithms are implemented using R, with tools like tidyverse for data manipulation and caret for model training and evaluation. The modeling process includes validation techniques, such as cross-validation, to ensure that the results are robust and generalizable.
A highlight of the project is its high accuracy, achieving an R² of 94%, reflecting the model’s strong predictive performance. This result demonstrates a well-optimized workflow from data preprocessing to model deployment. The project not only showcases practical applications in pricing strategies and market forecasting but also provides educational value by integrating key aspects of data science in a single workflow.
This comprehensive project is a testament to the power of combining technical expertise with real-world problem-solving and serves as an excellent example of applied data science for professionals and learners alike.
Summary
This project, titled "Customer Clustering and Attrition Analysis," explores advanced techniques in multivariate data analysis and predictive modeling, focusing on identifying and understanding patterns in customer behaviors and attrition. The analysis addresses two primary challenges often encountered in such studies: redundancy and relevance of variables.
To handle redundancy, the project employs strategies to identify and group highly correlated variables while minimizing correlations across groups, thereby enhancing the interpretability of the model. Relevance is addressed by evaluating the relationship between predictor variables and the target variable, ensuring that only meaningful features are included in the final analysis.
The project begins by setting up the working directory and loading essential libraries such as tidyverse and ggplot2, which are used for data manipulation and visualization. These tools provide a foundation for exploring and analyzing the customer data effectively.
Clustering methods are then applied to uncover natural groupings within the dataset, which helps in understanding customer dynamics. In addition, variable selection techniques are used to streamline the predictive modeling process and improve its accuracy. These methods are key to ensuring that the model captures the most relevant patterns without overfitting.
The project also integrates data visualizations, offering an intuitive understanding of the underlying patterns and relationships within the dataset. The clustering results allow for segmentation of customers into distinct groups, providing actionable insights for targeted interventions in customer retention and management.
Overall, this project aims to provide a comprehensive approach to customer clustering and attrition prediction. By combining advanced statistical methods with clear visual representations, it seeks to support decision-makers in implementing effective strategies for improving customer retention and satisfaction. The findings offer a valuable foundation for further analysis and real-world application of the insights derived from the data.
The project is an R-based movie recommendation system designed to provide personalized film suggestions to users. Its core objective is to leverage collaborative filtering techniques, a common approach in recommendation engines, which identifies patterns in user behavior or item attributes to predict preferences. The workflow begins with "data preparation", where datasets containing movie metadata and user ratings (likely from platforms like MovieLens) are loaded, cleaned, and merged. This step ensures the removal of missing values and inconsistencies, creating a reliable foundation for analysis. Following this, "exploratory data analysis" investigates trends in ratings, genre popularity, and user engagement, often visualized through graphs or heatmaps to highlight interactions between users and movies.
The "modeling phase" employs collaborative filtering methods, such as calculating similarity scores (e.g., cosine similarity) between users or movies to identify patterns. Tools like the `recommenderlab` package in R may facilitate matrix factorization or singular value decomposition (SVD) to reduce dimensionality and uncover latent features in the data. These models predict how users might rate movies they haven’t seen, enabling the system to generate "personalized recommendations"—typically a ranked list of top-N movies tailored to individual preferences.
To ensure robustness, the project includes "evaluation metrics" like RMSE (Root Mean Squared Error) to measure rating prediction accuracy or precision/recall to assess recommendation relevance. Built using R and libraries such as `dplyr` and `ggplot2`, the system demonstrates practical skills in data manipulation, machine learning, and algorithmic implementation. Ultimately, it serves as a portfolio-ready example of how data science can drive user-centric solutions, highlighting foundational competencies in building scalable recommendation engines. While specifics may vary, the project aligns with industry-standard practices for collaborative filtering and showcases end-to-end data science workflow.