The work of this repository was done at the course "Data Science", regarding the "Industrial Engineering" program at Universidad Tecnológica Nacional, Buenos Aires, Argentina.
00. Subtes-BuenosAires.ipynb: Work that corresponds to an Exploratory Data Analysis of the Subway network of Buenos Aires City.
Buenos Aires Government - Official Data.
Main tools:
EDA, Pandas, Seaborn, Matplotlib.
01. [scikit-learn] Regression Models - AirBnb in London.ipynb: the main objective of this work is to use a regression model to predict Airbnb prices in London according to a series of different features. At the end of the work, the different algorithms used are compared to see which one would have been more effective.
Main tools: -Pandas, EDA, Seaborn, Matplotlib, Scikit-Learn, Linear Regression, Support Vector Regressor, KNN, GridSearch, RandomForest Regressor.
Metrics used:
Accuracy, R2, MSE, MAE, ROC Curve.
02. PCA_kPCA_Breast_Ejercicios.ipynb:Use of algorithms to reduce dimensionality.
Main tools:
PCA, KernelPCA.
03. cluster-AI-2020__clustering_credit.ipynb: Work done, learning non supervised algorithms.
Main tools:
PCA, Clustering with K-Means, Hierarchical Clustering.
Metrics used:
Silhouette Score.