Machine Learning Projects
This project utilizes the Climate Trace CO2 emission dataset to develop a comprehensive emissions estimation model. The methodology encompasses data retrieval and summarization, exploratory and geospatial analysis, data cleaning and preprocessing. It also addresses missing data with robust imputation methods, including Iterative Imputer with Bayesian Ridge regression and MLPRegressor, ensuring data integrity and model reliability. Machine learning and deep learning techniques are employed alongside the exploration of Explainable AI to enhance model transparency. The performance of these models is compared to optimize accuracy in emissions forecasting.
This project presents an analysis of watershed data, incorporating a sophisticated series of data cleaning, normalization, and clustering techniques. The research harnesses an array of methods including EDA, normalization of features, and the use of HDBSCAN for clustering. The clustering process itself is rigorously detailed, involving multiple iterations and comparisons across different cluster numbers to fine-tune the models based on silhouette scores, Davies-Bouldin Index, and other metrics. This is followed by a comprehensive evaluation of clustering outcomes, utilizing techniques such as PCA for dimensionality reduction to better understand and visualize the data structure. The application of such an array of advanced analytical techniques showcases a thorough exploration and manipulation of the data to derive insightful conclusions.
This project demonstrates diverse approaches to modeling soybean yield using both tabular and raster data, employing a variety of statistical and machine learning techniques. Utilizing libraries like pandas, scikit-learn, TensorFlow, and xgboost, the analysis includes data preprocessing, visualization, and the application of normalization techniques. Advanced modeling strategies are executed, including Ridge and Lasso regressions with hyperparameter tuning via GridSearchCV, and XGBoost for further prediction optimization. It also explores feature importance and model diagnostics to improve predictions, highlighting its thoroughness and technical sophistication in tackling agricultural data science challenges
This data science project leverages the state-of-the-art XGBoost algorithm within a meticulously designed end-to-end machine learning pipeline. The project showcases data preprocessing techniques, ingeniously employing encoding methods such as Ordinal Encoder, OneHotEncoder, and DictVectorizer to transform complex categorical variables. Through rigorous hyperparameter tuning using both manual iteration and automated approaches like GridSearchCV and RandomizedSearchCV, the model's performance is optimized across an extensive hyperparameter space, exemplifying the technical prowess and attention to detail.
This Jupyter notebook delves into the intricacies of time series modeling and forecasting, masterfully employing sound statistical techniques such as the AutoCorrelation Function (ACF) and Partial AutoCorrelation Function (PACF) to unravel the underlying patterns in complex temporal data. The notebook showcases a comprehensive arsenal of time-series models, including the AutoRegressive (AR) model, Moving Average (MA) model, AutoRegressive Moving Average (ARMA) model, Seasonal AutoRegressive Integrated Moving Average (SARIMA) model, and the powerful Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, each meticulously tuned to capture the nuances of real-world time series phenomena. Through rigorous statistical tests like the Dickey-Fuller and Augmented Dickey-Fuller tests, the notebook adeptly identifies and transforms non-stationary data into stationary form, laying the foundation for robust and reliable forecasting.
This Jupyter notebook provides a detailed exploration of various machine learning algorithms using Python, with a focus on both supervised and unsupervised learning techniques. It thoroughly addresses classification and regression models, model tuning, and data preprocessing. The notebook also delves into regularization techniques such as Lasso and Ridge to optimize model performance and prevent overfitting. Additionally, it encompasses advanced data handling strategies, including pipeline construction for model validation and testing, showcasing a robust framework for practical data science applications.
