diff --git a/src/app/page.tsx b/src/app/page.tsx index c7ff8ad..5f2d217 100644 --- a/src/app/page.tsx +++ b/src/app/page.tsx @@ -24,7 +24,7 @@ export default function DataSciencePortfolio() { borderRadius="soft" contentWidth="mediumSmall" sizing="mediumSizeLargeTitles" - background="blurBottom" + background="circleGradient" cardStyle="layered-gradient" primaryButtonStyle="shadow" secondaryButtonStyle="layered" @@ -36,8 +36,7 @@ export default function DataSciencePortfolio() { { name: "Problem", id: "problem" }, { name: "Dataset", id: "dataset" }, { name: "Models", id: "models" }, - { name: "Impact", id: "impact" }, - { name: "About", id: "about" } + { name: "Impact", id: "impact" } ]} brandName="DS Portfolio" bottomLeftText="Data Science | Time Series" @@ -51,6 +50,7 @@ export default function DataSciencePortfolio() { description="Predicting Future Demand Using Time Series Models (ARIMA & Prophet)" tag="Data Science Project" tagIcon={TrendingUp} + background={{ variant: "plain" }} imageSrc="https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-modern-professional-data-science-dashb-1772907655784-bef0179a.png" imageAlt="Retail forecasting analytics dashboard" mediaAnimation="slide-up" @@ -58,7 +58,7 @@ export default function DataSciencePortfolio() { testimonials={[ { name: "Forecast Accuracy", handle: "Prophet Model", testimonial: "19% more accurate than ARIMA in predicting retail demand patterns with advanced seasonality handling", rating: 5, - imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png?_wi=1" + imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png" } ]} buttons={[ @@ -182,11 +182,11 @@ export default function DataSciencePortfolio() { }, { id: "2", name: "Demand Prediction Accuracy", role: "Forecast Precision", company: "Seasonality Insights", rating: 5, - imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png?_wi=1" + imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png" }, { id: "3", name: "Revenue Planning", role: "Margin Optimization", company: "Inventory Carrying Costs", rating: 5, - imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png?_wi=1" + imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png" } ]} kpiItems={[ @@ -222,16 +222,13 @@ export default function DataSciencePortfolio() { tagIcon={Zap} products={[ { - id: "1", brand: "Data Exploration", name: "Historical Sales Analysis", price: "Interactive", rating: 5, - reviewCount: "Daily", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png?_wi=2", imageAlt: "Historical sales data explorer" + id: "1", brand: "Data Exploration", name: "Historical Sales Analysis", price: "Interactive", rating: 5, reviewCount: "Daily", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png", imageAlt: "Historical sales data explorer" }, { - id: "2", brand: "Forecasting Tools", name: "30-Day Demand Predictor", price: "Real-time", rating: 5, - reviewCount: "Updated", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png?_wi=2", imageAlt: "Demand forecast visualization" + id: "2", brand: "Forecasting Tools", name: "30-Day Demand Predictor", price: "Real-time", rating: 5, reviewCount: "Updated", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png", imageAlt: "Demand forecast visualization" }, { - id: "3", brand: "Model Insights", name: "Prophet Components Analysis", price: "Interpretable", rating: 5, - reviewCount: "Detailed", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-decomposition-visualizatio-1772907654663-9d820912.png", imageAlt: "Seasonality components breakdown" + id: "3", brand: "Model Insights", name: "Prophet Components Analysis", price: "Interpretable", rating: 5, reviewCount: "Detailed", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-decomposition-visualizatio-1772907654663-9d820912.png", imageAlt: "Seasonality components breakdown" } ]} gridVariant="three-columns-all-equal-width" @@ -251,13 +248,13 @@ export default function DataSciencePortfolio() { { id: "expertise", groupTitle: "Expertise", members: [ { - id: "1", title: "Time Series Forecasting", subtitle: "ARIMA, Prophet, LSTM neural networks", detail: "Statistical and machine learning approaches to temporal data prediction", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png?_wi=2" + id: "1", title: "Time Series Forecasting", subtitle: "ARIMA, Prophet, LSTM neural networks", detail: "Statistical and machine learning approaches to temporal data prediction", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png" }, { - id: "2", title: "Business Analytics", subtitle: "Inventory optimization, demand planning", detail: "Translating data insights into actionable business strategies", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png?_wi=3" + id: "2", title: "Business Analytics", subtitle: "Inventory optimization, demand planning", detail: "Translating data insights into actionable business strategies", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png" }, { - id: "3", title: "Full-Stack Data Science", subtitle: "Python, SQL, visualization, deployment", detail: "End-to-end project execution from exploration to production", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png?_wi=4" + id: "3", title: "Full-Stack Data Science", subtitle: "Python, SQL, visualization, deployment", detail: "End-to-end project execution from exploration to production", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png" } ] } @@ -316,7 +313,7 @@ export default function DataSciencePortfolio() { { text: "Get in Touch", href: "mailto:contact@example.com" }, { text: "View More Projects", href: "#" } ]} - background={{ variant: "blurBottom" }} + background={{ variant: "plain" }} useInvertedBackground={false} />