This project explores how social media usage influences students’ academic performance, mental health, and sleep duration using statistical analysis and data visualization in R. The analysis includes exploratory data analysis (EDA), hypothesis testing (t-test, ANOVA, Chi-square), correlation analysis, and machine learning techniques to uncover meaningful behavioral patterns among students. Interactive visualizations and statistical insights were used to identify trends across gender, age groups, academic levels, and social media platforms

This project evaluates the effects of different dietary feed treatments on serum biochemical parameters and growth performance of broiler chickens using one-way ANOVA and post hoc analysis in R. The analysis includes data visualization, assumption testing, and interpretation of treatment effects on cholesterol concentration, glucose level, total protein, and final body weight.

A comprehensive business intelligence and predictive analytics project focused on customer transaction behavior in Blackwell Electronics. The study applies exploratory data analysis, feature engineering, classification models, regression techniques, and model evaluation to uncover actionable insights for improving customer targeting and sales strategy.

The aim of this project is look at various ways to accurately predict measurable minerals used in treatment of drinking water. sample drinking water was tested and recorded for 10 minerals. "Calcium.CA" "magnesium.Mg" "Sodium.Na" "Potassium" "Sulfates" "Chlorides" "Nitrates" "Nitrites" "dry.residues" "Bicarbonates" ##Task !developed a classification model to identify ideal mineral composition for water treatment using both WHO standards and data-driven approaches. While WHO guidelines served as the foundation benchmark, the decision tree and random forest models provided refined thresholds tailored to the data set. we shall be comparing the record with WHO standard to determine if those mineral element are present at recommended amount or in excess in our drinking water. we shall train our model to assist us to easily predict the recommended amount of mineral required.