Merge version_1 into main #2
@@ -24,7 +24,7 @@ export default function DataSciencePortfolio() {
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borderRadius="soft"
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contentWidth="mediumSmall"
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sizing="mediumSizeLargeTitles"
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background="blurBottom"
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background="circleGradient"
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cardStyle="layered-gradient"
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primaryButtonStyle="shadow"
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secondaryButtonStyle="layered"
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@@ -36,8 +36,7 @@ export default function DataSciencePortfolio() {
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{ name: "Problem", id: "problem" },
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{ name: "Dataset", id: "dataset" },
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{ name: "Models", id: "models" },
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{ name: "Impact", id: "impact" },
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{ name: "About", id: "about" }
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{ name: "Impact", id: "impact" }
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]}
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brandName="DS Portfolio"
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bottomLeftText="Data Science | Time Series"
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@@ -51,6 +50,7 @@ export default function DataSciencePortfolio() {
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description="Predicting Future Demand Using Time Series Models (ARIMA & Prophet)"
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tag="Data Science Project"
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tagIcon={TrendingUp}
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background={{ variant: "plain" }}
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imageSrc="https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-modern-professional-data-science-dashb-1772907655784-bef0179a.png"
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imageAlt="Retail forecasting analytics dashboard"
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mediaAnimation="slide-up"
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@@ -58,7 +58,7 @@ export default function DataSciencePortfolio() {
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testimonials={[
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{
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name: "Forecast Accuracy", handle: "Prophet Model", testimonial: "19% more accurate than ARIMA in predicting retail demand patterns with advanced seasonality handling", rating: 5,
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png?_wi=1"
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-professional-headshot-photograph-of-a--1772907654284-907207b0.png"
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}
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]}
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buttons={[
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@@ -182,11 +182,11 @@ export default function DataSciencePortfolio() {
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},
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{
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id: "2", name: "Demand Prediction Accuracy", role: "Forecast Precision", company: "Seasonality Insights", rating: 5,
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png?_wi=1"
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-time-series-visualization-showing-reta-1772907655531-6d88730e.png"
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},
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{
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id: "3", name: "Revenue Planning", role: "Margin Optimization", company: "Inventory Carrying Costs", rating: 5,
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png?_wi=1"
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imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3AQ8Q718DwJVd8ARNbVYjSdJYfR/a-forecast-visualization-showing-histori-1772907654869-c143cdcb.png"
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}
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]}
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kpiItems={[
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@@ -222,16 +222,13 @@ export default function DataSciencePortfolio() {
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tagIcon={Zap}
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products={[
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{
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id: "1", brand: "Data Exploration", name: "Historical Sales Analysis", price: "Interactive", rating: 5,
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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"
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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"
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},
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{
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id: "2", brand: "Forecasting Tools", name: "30-Day Demand Predictor", price: "Real-time", rating: 5,
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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"
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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"
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},
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{
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id: "3", brand: "Model Insights", name: "Prophet Components Analysis", price: "Interpretable", rating: 5,
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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"
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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"
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}
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]}
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gridVariant="three-columns-all-equal-width"
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@@ -251,13 +248,13 @@ export default function DataSciencePortfolio() {
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{
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id: "expertise", groupTitle: "Expertise", members: [
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{
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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"
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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"
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},
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{
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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"
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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"
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},
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{
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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"
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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"
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}
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]
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}
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@@ -316,7 +313,7 @@ export default function DataSciencePortfolio() {
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{ text: "Get in Touch", href: "mailto:contact@example.com" },
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{ text: "View More Projects", href: "#" }
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]}
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background={{ variant: "blurBottom" }}
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background={{ variant: "plain" }}
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useInvertedBackground={false}
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/>
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</div>
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