<![CDATA[In the rapidly evolving mobile application ecosystem, enhancing user experience on the Google Play Store has become a critical challenge due to the vast number of available applications. This study proposes an integrated approach combining Logistic Regression, Min-Max Scaling, and the Term Frequency–Inverse Document Frequency (TF-IDF) Vectorizer to classify applications and generate personalized recommendations. The dataset, obtained from the Google Play Store, includes numerical features such as ratings, size, and installs, as well as textual data from user reviews. Min-Max Scaling was applied to normalize numerical attributes, ensuring balanced feature contributions during model training. TF-IDF was employed to convert textual reviews into meaningful numerical representations, enabling the model to capture the semantic importance of terms. The classification and recommendation system was evaluated using accuracy, precision, and recall as performance metrics. Experimental results demonstrated a substantial improvement compared to the baseline model, with accuracy, precision, and recall reaching 99.8%, compared to the previous 22.8% baseline performance. The system effectively recommended relevant applications based on user preferences, as measured through cosine similarity in feature space. These results indicate that the proposed method not only improves classification accuracy but also enhances the quality of app recommendations, thereby significantly improving user experience. The findings contribute to the field of computer science by demonstrating an effective integration of feature scaling and text vectorization into a classical machine learning model, offering a scalable and interpretable solution for large-scale recommendation systems in digital marketplaces. This approach can be further adapted to other domains requiring hybrid processing of numerical and textual data for predictive analytics.]]>
