🚀 Key Contributions & Features from Radu Bizga Nicolescu:
1️⃣ Machine Learning & Data Science Techniques ✅ Multiple Linear Regression – Built a predictive model for NYC rent prices using scikit-learn. ✅ Feature Engineering – Selected and processed continuous & discrete variables to improve model accuracy. ✅ Data Visualization – Created insightful box plots, scatter plots, and correlation analyses with Seaborn & Matplotlib.
2️⃣ Distance Metrics & Similarity Analysis ✅ Implemented Key Distance Metrics for machine learning:
Euclidean Distance – Standard geometric distance calculation. Manhattan Distance – Useful for grid-based paths. Hamming Distance – Measures binary string differences. ✅ Optimized with SciPy – Used scipy.spatial.distance for efficient computations. 3️⃣ k-Nearest Neighbors (k-NN) Algorithm ✅ Built a k-NN Distance Function for comparing movies based on attributes. ✅ Extended to N-Dimensions – Generalized the function for high-dimensional data.
4️⃣ Data Normalization for Better Comparisons ✅ Implemented Min-Max Normalization – Scaled movie release dates and numerical data between 0 and 1 for consistency.
5️⃣ Code Optimization & Performance Improvements ✅ Refactored Loops for Efficiency – Improved calculations with optimized Pythonic implementations. ✅ Error Handling & Input Validation – Ensured robustness by handling dimension mismatches in distance functions.
See www.codecademy.com/learn/paths/data-science for a list of the data and code we've published:
Unless otherwise noted, our datasets are available under the Creative Commons Attribution 4.0 International License, and the code is available under the MIT License.