Predictive Analysis of Customer Behavior in E-Commerce: Prediction of Average Order Value and Identification of High-Value Customers

Description:

This data analysis project explores customer behavior on an e-commerce platform using a dataset containing key metrics such as session duration, product detail views, app transactions, add-to-cart rate per session, discount rate per visited products, credit card info saving, average order value (“avg order value”), and a high-value customer indicator (“high_value_customer”). The code is structured in several steps:

Data Preparation: Loading from the clipboard, cleaning (replacing commas with periods for decimals), numeric conversion, and encoding of categorical variables (e.g., yes/no via LabelEncoder).

Regression Modeling: Use of an XGBoost model to predict average order value, with evaluation via RMSE and R² on a test set (30% of the data). Visualizations include a scatter plot of predictions vs. actual values, a correlation matrix, a boxplot of average basket by card saving, and a histogram of prediction errors.

Classification Modeling: Logistic regression with L2 regularization to identify high-value customers, based on selected features (session duration, product views, etc.). Evaluation via ROC-AUC score and ROC curve.

Abdi-Basid ADAN, 09–2025

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

👉Click here now! : https://github.com/abdibasidadan-byte

Climatic and Environmental Drivers of NDVI Dynamics in an Arid Region: Predictor Importance, Correlation, and Trend Analysis (1987–2022)

The variables used in the analysis encompass vegetation, climate, and environmental conditions in the Godoria region. The Normalized Difference Vegetation Index (NDVI) reflects vegetation status derived from satellite data, while soil moisture and land surface temperature (LST) provide information on water availability and surface heat. Climatic factors include rainfall, the Standardized Precipitation Index (SPI) as a measure of drought conditions, and both minimum and maximum temperatures (Temp Min and Temp Max), which influence plant physiology and evapotranspiration. Potential evapotranspiration (PET) indicates the potential water deficit driven by climatic conditions. Finally, sea level serves as a relevant oceanographic indicator in the arid coastal context of Djibouti, with potential indirect impacts on local climate dynamics and water availability.

(a)

(b)

Fig. 1. (a)Estimation of relative importance of climate variables as predictors of NDVI. (b) Pearson correlation coefficient between NDVI and climate variables.

Table 1. Mann Kendall Trend and Theil-sen slope statistics of NDVI and climate variables from 1987 to 2022.

	Theil-sen slope	p.value	Mann kendall tau (τ)	p.value
NDVI	-0.001	0.858	-0.023	0.859
Sea level	0.001	0.646	0.057	0.646
Rainfall	0.000	NA	0.000	NA
SPI	0.000	0.917	0.000	0.917
Temp Min	0.057	0.109	0.194	0.109
Temp Max	0.072	0.173	0.166	0.172
Soil Moisture	-0.030	0.739	-0.057	0.739
PET	0.046	0.649	0.085	0.649
LST	-0.222	0.720	-0.111	0.720

Abdi-Basid ADAN, 2024

Exploiter les données SRTM pour la cartographie (ArcGIS)

Découvrez dans ce tutoriel complet comment exploiter les données d’élévation SRTM pour vos projets SIG et cartographiques. De la téléchargement des rasters à leur importation et extraction par shapefile, en passant par la représentation visuelle et la mise en page professionnelle, vous serez guidé pas à pas pour transformer des données brutes en cartes prêtes à l’analyse ou à la présentation. Que vous soyez étudiant, chercheur ou passionné de géomatique, ce tutoriel vous donnera les clés pour gagner du temps et améliorer la qualité de vos productions cartographiques.

Abdi-Basid ADAN, 2025

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

👉Click here now: https://www.youtube.com

En collaboration avec :

Interannual Variability of Sea Level and Sea Surface Temperature in Djibouti and Yemen: Insights from the University of Hawaii Sea Level Center and NOAA OISST Data (1981–2022)

Figure 1. Interannual sea level changes in Djibouti and Yemen over the period 2007 to 2018 from University of Hawaii, Sea Level Center

Figure 2. Annual average Sea surface température (°C) from 1981 to 2022 , NOAA OISST.

Abdi-Basid ADAN, 2022

The Abdi-Basid Courses Institute

Spatiotemporal Analysis of the Impacts of Early 2024 Wildfires in North America: A Study of Correlations Between Meteorological Conditions, Air Quality, and Wind Dynamics in Stinnett and Canadian, T

This study provides a detailed spatiotemporal analysis of the impacts of early wildfires occurring between February 26 and March 16, 2024, in North America, with a focus on the localities of Stinnett (35.82°N, -101.44°W) and Canadian, TX (35.91°N, -100.38°W). Utilizing data from NetCDF files (v10m, u10m, AOD500nm, t2m, coplev, comlev), the analysis employs the xarray library to process meteorological and atmospheric variables, including aerosol optical depth (AOD at 550 nm), 2-meter temperature (converted to °F), carbon monoxide (CO) at 1000 hPa and model level 1 (in ppb), and 10-meter wind components (u10, v10). Visualizations, created using matplotlib and cartopy, include spatial maps of daily averages of AOD, temperature, and CO, overlaid with wind vectors to illustrate their role in pollutant dispersion. Time series analyses reveal the daily evolution of these variables for both localities, highlighting significant AOD peaks in Stinnett, indicative of smoke plume passage. Hourly heatmaps confirm the immediate impact of wildfires on air quality, showing a marked increase in AOD within hours. Wind rose diagrams, generated for each locality, quantify the frequency and intensity of winds, emphasizing their influence on pollutant spread. Finally, a pixel-by-pixel Pearson correlation analysis between AOD and CO reveals a strong positive relationship (R close to +1) in affected areas, confirming the common origin of aerosols and CO from biomass burning. These findings, contextualized within an extreme meteorological event characterized by high temperatures and dry conditions, underscore the complex interactions between meteorological and atmospheric dynamics, with implications for air quality monitoring and environmental crisis management in North America.