Laptop Recommendation System

As part of our Data Analysis course at the Faculty of Artificial Intelligence, we worked as a team to build a Laptop Recommendation System using real-world data and a complete data science workflow — from raw data to insightful predictions!

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Technologies & Tools Used:

Python, Pandas, NumPy, Matplotlib, Seaborn, Streamlit

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? Project Workflow Included:

Data Cleaning & Preprocessing

• Handled missing values, encoded categorical data, treated outliers, and applied feature scaling.

Data Visualization

• Used Matplotlib and Seaborn to extract trends and patterns from the dataset.

? Model Training & Comparison

• Applied train-test split and k-fold cross-validation to compare performance across different evaluation strategies.

? Machine Learning Algorithms

• Tested and compared KNN, Random Forest, and SVM classifiers.

Model Evaluation Metrics

• Accuracy

• Precision

• Recall

• F1-Score

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The entire system was wrapped in a user-friendly Streamlit app, allowing users to interact with our model and receive laptop recommendations tailored to their needs.