2025-12-30

NCAA Match Prediction Script Using Logistic Regression

This script aims to predict the probability that a college basketball team wins against another in a match. It leverages historical data on teams, regular season results, and tournament seeds to build a predictive model.

Purpose :
- Understand how to transform raw match data into features usable
by a machine learning model.
- Build a supervised model capable of predicting the match winner.
- Evaluate the model using standard metrics (log loss, ROC-AUC)
and apply it to tournament simulations.

Data :
- "teams": team information (TeamID, TeamName, first and last
Division 1 season)
- "results": regular season match results (winning team, losing team,
score, match day)
- "seed_round_slots": information on tournament seeds and match slots

Variables:
- "team_stats": number of wins and losses per team per season
- "match_data": prepared match dataset for model training
- "X", "y": features and target for training
- "model": trained logistic regression model
- "matchup_example": sample tournament matches for prediction

Model:
- Logistic Regression
- It is supervised because it learns from labeled data: each historical
match has a label "1" if Team1 wins, "0" otherwise.
- Suitable for binary classification and allows estimating the probability
of a team winning.

Objectives:
1. Load the necessary CSV files.
2. Compute wins and losses for each team and season.
3. Create a match dataset ready for training.
4. Normalize the data and split into training and test sets.
5. Train a supervised Logistic Regression model.
6. Evaluate the model using log loss and ROC-AUC.
7. Prepare a sample tournament matchup and predict win probabilities.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! :  https://github.com/




    
The Abdi-Basid Courses Institute (tABCi)

LinkedIn group page: https://www.linkedin.com/groups/ 

🏛️ tABCi Laboratories :


đŸ§Ș Abdi-Basid ADAN LABS  Focus: Interdisciplinary Sciences & Education.

🧠 The Deep Thinking Lab Focus: Philosophy & Human Sciences.

🌍 The EcoClimate Hub Focus: Climate Science & Environment.



@2025 Abdi-Basid ADAN

2025-12-24

Innovative Discoveries on the Laws of Cosmology and Humanity: New Perspectives on the World


A B S T R A C T : This study proposes an innovative framework for exploring the laws of human cosmology by modeling consciousness as a dynamic entity integrating six fundamental sensory dimensions (taste, vision, smell, hearing, internal/external touch, and soul). Inspired by the treatise MystĂšres DĂ©voilĂ©s : VĂ©ritĂ©s Novatrices – RĂ©vĂ©lations des Lois de la Cosmologie de l’Homme (Adan, 2025), the aim is to link consciousness to parallel realities through sensory vibrations, while emphasizing the supreme position of human beings within nature. An observational and contemplative methodology was employed, combining a lit- erature review in neuroscience and philosophy with immersive observations in African and European societies (2020–2025), along with meditative reflections accounting for variations related to age, gender, and cultural context. The findings indicate that consciousness emerges as a dual transverse wave, evolving from a fetal pseudo-consciousness to a mature postnatal form, with passive anomalies such as hypnosis or hallucinations. It interacts with the environment through six senses, generating vibrations that give rise to parallel realities; neurons function as a bridge between body and soul, whereas sleep illustrates vital discontinuity. At the cosmic scale, consciousness links life and the universe, influenced by time and predestination. These discoveries offer a unified model between the material and immaterial realms, with implications for scientific philosophy, addiction prevention, and the preservation of human dignity as a supreme ethical responsibility.

Keywords: Adanian Theories Sensory Vibrations Parallel Realities Soul and Gravity Consciousness Human Cosmology Scientific Philosophy


🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : 







Adan, A.-B. (2025). Innovative Discoveries on the Laws of Cosmology and Humanity: New Perspectives on the World. https://doi.org/10.5281/zenodo.18035321

2025-11-02

MystĂšresDĂ©voilĂ©s :VĂ©ritĂ©s NovatricesRĂ©vĂ©lations des Lois de la Cosmologiede l’Homme : Nouvelles Visions du Monde

Cet ouvrage, Ă  la dimension d’un traitĂ© philosophique, au sens propre, est amenĂ© avant tout, de dĂ©rouler une variĂ©tĂ© des structures en concepts Ă  travers des vĂ©racitĂ©s fondĂ©s sur un ensemble des raisonnements dĂ©ductifs, observationnels et logiques, entretenant des perspectives transversales, longitudinales et pyramidales pour mettre en Ɠuvre non seulement des solutions transparentes Ă  l’Ă©gard des problĂšmes complexes, mais aussi formuler des sous-ensembles des thĂ©orĂšmes, propriĂ©tĂ©s, axiomes, postulats, assomptions et hypothĂšses, qui s’agglomĂšrent pour Ă©difier Ă  leur tour les piliers des thĂ©ories Adaniennes.

En termes simples, cet ouvrage initie l’universalitĂ© de la scientificitĂ© en philosophie, en mettant sur pied des prĂ©ceptes dont la plupart sont novatrices, afin d’Ă©manciper la multiplicitĂ© en recherche dans le domaine de la philosophie et les disciplines adjacentes et d’autres part, redĂ©finir les bases connues en principes de la littĂ©rature en philosophie. Les thĂ©ories dĂ©veloppĂ©s et amĂ©liorĂ©s dans cet ouvrage sont Ă  la fois amplificatrice et simplificatrice pour une accessibilitĂ© Ă  un large public leur offrant une vue d’ensemble Ă  360°.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! : 

2025-10-17

Modeling Rainfall Variability and Extremes in Djibouti under CMIP5, CMIP6, and CORDEX Scenarios

Description:

This study investigates historical and projected rainfall patterns at Djibouti Airport using observational datasets (CHIRPS, ERA5Land) and climate models, including CanESM2, CanESM5, and regional CORDEX downscaled simulations. The research evaluates model performance through statistical metrics, such as Taylor diagrams and extreme precipitation indices (ETCDDI), and examines projected changes under multiple climate scenarios (RCP4.5, RCP8.5, SSPs). The results provide insights into future rainfall variability, trends in extreme events, and potential drought and wet periods, supporting climate risk assessment and adaptation planning in the Horn of Africa.



Figure 1. Historical CHIRPSv.2 and ERA5Land annual rainfall at Djibouti airport station compared to corrected rainfall from CanESM2 and RCM (AFR22-CanRCM4, AFR44-SMHI-RCA4, AFR44-CanRCM4, AFR44-UQAM-CRCM5) over the period 1980-2005 and Taylor Diagram for performance comparison (left panels).The radial coordinate indicates the variance ratio between the observation and the satellite data.




Figure 2. Projected rainfall changes relative to the baseline period 1953–2021 based on CanESM2-CORDEX, the downscaled CanESM2-CMIP5 and CanESM5-CMIP6 using Observation, CHIRPS and ERA5Land datasets. Colored shaded areas represent the areas of uncertainty (standard deviation) for the scenarios RCP4.5, RCP.8.5 for CMIP5 and SSP2-RCP4.5and SSP5-RCP8.5 for CMIP6 (right panels). The solid sphere in the boxplot represents the mean, and the interquartile range spans from Q1 to Q3 within the box square. Horizontal lines above and below denote the minimum and maximum. Irregular dotted indicate extreme values (left panels).


Table 1. Overall change of rainfall for future climate generated using CanESM2, AFR44 and CanESM5 models in RCP 4.5 and RCP 8.5 scenarios.






Figure 3. Historical and projected average monthly rainfall at Djibouti airport station (1953-2021) using statistical downscaling of CanESM2 and CanESM5 and corrected regional model CORDEX from 2006–2100.


Table 2. Trend of ETCDDI indices for extreme precipitation (historical (1980-2017) and projected with AFR44.CanRCM4.CHIRPS.RCP45 (2018-2099) at the Djibouti airport station and detection of the stationarity period with the Pettitt test (Pettitt, 1979) and standard normal homogeneity (SNHT, Alexandersson 1986).


Figure 4. Projected Interannual variations (line) and linear trends (dash) in mean annual 3-month RAI at Djibouti airport station from downscaled CMIP5, CMIP6 and CORDEX based on observation, CHIRPS and ERA5Land datasets (right panels) and Characteristics of drought (left panels).


Figure 5. Variation of ETCDDI indices for projected extreme precipitation (2018-2099) at the Djibouti airport station with the CanESM2 (CHIRPS.RCP45 and ERA5Land. RCP85), CanESM5 (CHIRPS. RCP45 and ERA5Land. RCP45) and CORDEX (CHIRPS. RCP45 and ERA5Land. RCP45) models.







Figure 5. Projected rainfall changes relative to the baseline period 1953–2021 based on the downscaled CanESM2-CMIP5 and CanESM5-CMIP6 using Observation, CHIRPS and ERA5Land datasets. Colored shaded areas represent the areas of uncertainty (standard deviation) for the scenarios RCP1.9, RCP.2.6 and RCP.7.0 for CMIP5 and SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5 for CMIP6. The solid sphere in the boxplot represents the mean, and the interquartile range spans from Q1 to Q3 within the box square. Horizontal lines above and below denote the minimum and maximum. Irregular dotted indicate extreme values.


Table 3. Evaluation of precipitation projections (very short term) from 2006 to 2021 under the RCP 4.5 scenario simulated at the Djibouti airport station by Canadian Earth System models derived from CMIP5, CORE-CORDEX and CMIP6.



Table 4. Evaluation of precipitation projections (very short term) from 2006 to 2021 under the RCP 8.5 scenario simulated at the Djibouti airport station by Canadian Earth System models derived from CMIP5, CORE-CORDEX and CMIP6.



Table 5. Overall change of average rainfall for future climate generated using CanESM2, AFR44 and CanESM5 model RCP 4.5 scenarios.


RAINFALL TRESHOLD    (Djibouti Case)

·         Q12.5%    less than     35.75 mm/annum    (Dry events)

·         Q87.5%    more than   291.60/annum         (Wet events)

·         Q50%       less than      1 mm/month          (Dry events)

·         Q90%       more than   32.72 mm/month    (Wet events)

·         Light rainfall (0–0.2(q95) mm/day)

·         Moderate rainfall events (0.2–8(q99) mm/day)

·         Heavy rainfall events (> 8 mm/day).





LinkedIn group page: https://www.linkedin.com/groups/

🏛️ tABCi Laboratories :


đŸ§Ș Abdi-Basid ADAN LABS  Focus: Interdisciplinary Sciences & Education.

🧠 The Deep Thinking Lab Focus: Philosophy & Human Sciences.

🌍 The EcoClimate Hub Focus: Climate Science & Environment.

© 2024 Abdi-Basid ADAN. All rights reserved.

2025-10-03

Geospatial Assessment of Mangrove Dynamics in Relation to Climate Variability

Understanding changes in mangrove ecosystems driven by human activities, climate change, and environmental variations is essential for effective ecological management. The study analyze the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) and their responses to parameters such as sea level (SL), Potential Evapotranspiration (PET), rainfall (RF), Standardized Precipitation Index (SPI-1 month), soil moisture (SM), minimum temperature (TN), and maximum temperature (TX) within the study area. Trends, relative influences, spatial autocorrelation, and relationships between NDVI and climatic-environmental variables, as well as partial correlations, were analyzed using the Mann-Kendall monotonic trend test (MKMT), Relative Weight Analysis (RWA), partial correlation coefficients (PCC), and Multiple Linear Regression (MLR) methods. The spatiotemporal patterns of NDVI, EVI, and SAVI display similar dynamics, showing a reduction in bare soil and an increase in sparse and dense vegetation from 1987 to 2022. Nevertheless, zones of degradation were observed, particularly in southern in 2022 compared to 1987, as indicated by NDVI, EVI, and SAVI. These zones coincide with areas of increased salinity concentration according to the VSSI index over the same period. A notable deterioration in NDVI (> 0.2) was recorded from 2000 to 2012. The interannual trend of NDVI is slightly declining. Additionally, analyses with Mann-Kendall and Theil-Sen slope reveal that TN, TX, PET, and SPI-1 show increasing trends, though not statistically significant, while SM and LST show decreasing trends. For environmental variables, SL indicates an upward trend. Further, partial correlation analysis identifies SL, TN, SPI-1, TX, and PET as the primary climatic factors controlling vegetation dynamics during the JJAS season, with PCC values of -0.89, 0.87, 0.77, 0.76, -0.75, and 0.86 with NDVI, respectively. These findings underscore the significant influence of select environmental factors on the spatiotemporal dynamics of mangrove vegetation, providing insights critical for conservation and management efforts.

🎯 The detailed methodology and results can be accessed through this link:

👉click here now! :  https://www.impactio.com/publication



The Abdi-Basid Courses Institute (tABCi)


2025-09-23

Performance evaluation of very high multi-satellite daily precipitation products against Observation in situ weather from 1980 to 2021.

 The study covers a range of datasets, including satellite-based, hybrid (satellite + gauge), and reanalysis products:

  • CHIRPS v2 (0.05°, 1981–present): Hybrid product combining infrared satellite data with rain gauge observations.

  • GSMaP PRT V6 (0.1°, 2000–present): Satellite-based product using passive microwave and infrared sensors.

  • IMERG LR & FR (0.1°, 2000–present): Satellite-based datasets from NASA’s GPM mission; Late Run provides near–real-time data, while Final Run is bias-corrected with gauge data.

  • TRMM 3B42 (0.25°, 1998–2019): Satellite-derived dataset from the Tropical Rainfall Measuring Mission.

  • MSWEP v2.8 (0.1°, 1979–present): Multi-source hybrid product merging gauge, satellite, and reanalysis data.

  • ERA5 (0.25°, 1979–present): Global reanalysis from ECMWF.

  • ERA5-Land (0.1°, 1981–present): High-resolution reanalysis optimized for land-surface applications.

  • ERA5-Ag (~0.1°, 1979–present): Reanalysis dataset derived from ERA5, tailored for agricultural applications.







Abdi-Basid ADAN, 2023



LinkedIn group page: https://www.linkedin.com/groups/ 

🏛️ tABCi Laboratories :


đŸ§Ș Abdi-Basid ADAN LABS  Focus: Interdisciplinary Sciences & Education.

🧠 The Deep Thinking Lab Focus: Philosophy & Human Sciences.

🌍 The EcoClimate Hub Focus: Climate Science & Environment.