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airline_ontime_performance_project

Different feature selection methods like SVD, SelectKBest, RFE are used to choose best features, then different machine learning algorithms like Random Forest, Gradient Boosting Tree, XGBoost together with GridsearchCV etc are applied and compared to choose the good model which is the best fit for the dataset.

avocado_price_prediction

A variety of regression models (such as Random Forest, XGBoost, LightGBM) and time-series models (like Arima, Prophet, HoltWinters) are utilized to predict average price of avocados.

counting_cars

Counting cars on highways in videos is conducted by using the pre-trained YOLO4 multiple object detection algorithm (which is a deep learning model) with OpenCV and the centroid tracking algorithm.

customer_segmentation

Different unsupervised machine learning algorithms such as RFM, K-means, Spectral Clustering, GMM etc are used to classify customers into different meaningful clusters of customers.

demo-wandb

gutenberg_author_classification_project

Different supervised and unsupervised learning algorithms (including RNN deep learning network) together with NLP transformers are used to do text-author classification from Gutenberg project.

recommendation-system-project

In this project, a Recommender System is built from 2 popular methods which are Content Based Filtering (Gensim and Cosine Similarity algorithms) and Collaborative Based Filtering (Alternating Least Square model in PySpark). Then, this recommender system is deployed onto Heroku cloud platform.

tmdb_movie_analytic_project

Very detailed exploratory data analysis is executed on the dataset. Univariate and bivariate analysis using ANOVA and Chi-Squared Test between continuous and categorical variables are explored to find out the relationship between input variables and the output target 'revenue'.