Zulfiqar Ali Mir (PhD)’s Post

I would like to share new #MOOC Certificate of #Spatial #data #Science #What is spatial data science?, Data analysis, Data engineering, Data visualization.. #Predictive analysis, Forest-based and Boosted classification and Regression.. #Suitability modelling in data science.. #Pattern detection and Clustering, map subjectivity and inferential statistics, #Hot spot and outlier analysis. #AI, ML, DL....Neural networks, instant segmentation, object detection, image classification, pixel(Semantic) #segmentation. #Communicating results, ArcGIS #storyMaps. 🙏 ESRI#MOOC.

🚀💡 T-SNE: A Powerful Dimensionality Reduction Tool in Machine Learning 💡🚀 I wanted to share some insights on a fascinating technique in the world of machine learning: T-SNE (t-Distributed Stochastic Neighbor Embedding)! T-SNE is a dimensionality reduction algorithm that helps us visualize high-dimensional data in a lower-dimensional space, usually 2D or 3D, making it easier to understand and communicate the results to stakeholders2. It's particularly useful when dealing with complex data that doesn't lend itself well to linear methods like PCA (Principal Component Analysis) or ICA (Independent Component Analysis). The magic behind T-SNE lies in its ability to preserve the pairwise proximity of data points in the original high-dimensional space when mapping them to the lower-dimensional space. This is achieved by maximizing the similarity between the probability distributions of the pairs of data points in both spaces3.T-SNE has gained popularity in recent years, especially for its ability to create visually impressive plots that can help us better understand the structure of our data. It's a great tool for data exploration and can be a real conversation starter at data science gatherings! Here are some key points to remember about T-SNE: Dimensionality reduction: T-SNE helps us visualize high-dimensional data in a lower-dimensional space. Preserves proximity: T-SNE maintains the pairwise proximity of data points when mapping from high to low dimensions. Probability distribution: T-SNE maximizes the similarity between the probability distributions of the pairs of data points in both spaces. Visualization: T-SNE is excellent for data exploration and creating visually appealing plots. #MachineLearning #DimensionalityReduction #tSNE #DataVisualization #DataScience #LinkedInLearning #MLAlgorithms #DataExploration #DataAnalysis #DataScienceCommunity

Titanic Survival Prediction: 🚢 If you’re in the data science field and haven’t tackled the Titanic dataset, then you’re not truly a data scientist! 😅 I dove into this classic challenge to predict which passengers might have survived, and it was an exhilarating ride. 🌊📊 🧹 Data Exploration & Cleaning: I started by cleaning up the data—saying goodbye to irrelevant columns like 'Cabin', 'PassengerId', and 'Ticket'. I tackled missing values with some creative solutions, filling in gaps and ensuring everything was shipshape. 🔍 Feature Engineering: Next up, I created new features such as 'family' and categorized them into 'family_size' (Alone, Small, Large) to better understand the data. I used visualizations to uncover patterns and get a clearer picture of survival rates. 🤖 Model Building & Enhancement: I kicked things off with a Decision Tree Classifier, which gave me a respectable 76% accuracy. But I didn’t stop there—by switching to a Random Forest Classifier, I boosted the accuracy to a fantastic 83%! 🚀 📈 Final Touches: I wrapped up by generating predictions and preparing them for submission. This project was an exciting voyage through data science, blending creativity with analysis. I’m eager to set sail on new data adventures and would love to hear your thoughts, suggestions, or any tips you might have! Now it's time to meet with some UI with Machine learning. #titanic #titanicdata #prediction #dataset #ALML #MachineLearing #datascience #powerbi #UIUX #dataanalytics #dataengineer #statastics #dataanalysis

In data science, distance metrics quantify the similarity or dissimilarity between data points. These metrics play a crucial role in various machine learning tasks, including classification, clustering, and recommendation systems. Choosing the right distance metric is essential for accurate results. An inappropriate metric can distort data relationships, leading to flawed models. For instance, using Euclidean distance in text analysis might not capture the semantic similarity between words as effectively as cosine similarity. 1. Euclidean Distance: The Straight Shooter Measure the straight-line distance between two points. Simple, yet effective! 2. Manhattan Distance: The City Navigator Calculate the sum of absolute differences. Like navigating city streets, block by block! 3. Minkowski Distance: The Power Player Generalize Euclidean and Manhattan distances with a power twist. Flex your data muscles! 4. Cosine Similarity: The Text Whisperer Measure the cosine of the angle between vectors. Uncover hidden text relationships! 5. Jaccard Distance: The Set Sleuth Uncover similarities between sets. Reveal the secrets of intersection and union! 6. Hamming Distance: The String Sleuth Count the differences between strings. Crack the code of similarity! 7. Mahalanobis Distance: The Covariance Crusader Tackle covariance and find robust distances. Unleash the power of multivariate analysis! 8. Levenshtein Distance: The Edit Master Measure the minimum operations to transform strings. Edit your way to similarity! 9. Kullback-Leibler Divergence: The Probability Pro Measure the difference between probability distributions. Uncover hidden patterns! 10. Bray-Curtis Dissimilarity: The Ecological Explorer Uncover similarities in ecological and biological data. Venture into new territories! #DataScience #Data_Science #Distance_Meteics #Distance_Measurement #Data_Analysis

Discover how Complexity Science fosters interdisciplinary creativity in data visualization, blending art, science, and diverse professional skills to explore new ways of presenting complex scientific data. Check out the latest piece by Liuhuaying Yang and Paul Kahn. https://lnkd.in/eexsbk3x #DataViz #DataVisualization #Creativity

Screen-reader users can upload a dataset and create customized data representations that combine visualization, textual description, and sonification. | Click below to read the full article on Sunalei

 #storytellingwithdata It's a masterpiece! Simple is harder than complex and no doubt took a lot of effort to simplify a complex problem to tell a story. Whether we can improve it or not depends on the audience it was intended for. For those unfamiliar with the content it will require a greater cognitive load and mental effort to decipher the information presented as opposed to those already familiar with the context.   It’s a common challenge having to communicate to people with disparate needs at once e.g. across technical disciplines, investors, employees and government bodies. As a result you end up in a position where you can’t communicate to any one of them as effectively as you could if you narrowed your target audience. Sometimes this means creating different communications for different audiences. The more you know about your audience, the better positioned you’ll be to understand how to resonate with them and form a communication that will meet their needs and yours.

Understanding dot product, Euclidean distance, and cosine similarity is crucial for anyone working with data. Discover the importance of these mathematical techniques in this insightful article. #datamath #techknowledge #SingleStore

🌍 Harnessing the Power of Machine Learning in GIS🌍 As we stand on the precipice of a technological revolution, the integration of Machine Learning into GIS is transforming the way we analyze and interpret spatial data. This synergy is not just a trend; it is paving the way for groundbreaking advancements across various sectors. 🔍 Enhanced Data Analysis: Traditional GIS methods can be enhanced by ML algorithms, allowing for more sophisticated data analysis. By leveraging vast datasets, machine learning models can identify patterns and trends that would be nearly impossible to discern through conventional means. 📈 Predictive Analytics: The incorporation of ML into GIS enables predictive analytics, equipping organizations to make data-driven decisions. Whether it's forecasting urban growth, environmental changes, or disaster response strategies, the ability to predict outcomes based on spatial data significantly enhances strategic planning. 🌱 Sustainable Development: In the realm of urban planning and environmental management, ML-driven GIS applications are vital for promoting sustainability. By analyzing land use patterns and environmental impacts, stakeholders can better understand how to balance development with ecological preservation. 🚀 Real-Time Insights: The combination of ML and GIS empowers real-time data processing and visualization. This capability is essential for industries such as transportation, where real-time traffic analysis and route optimization can greatly improve efficiency and reduce congestion. 🤝 Collaboration Across Fields: The intersection of ML and GIS fosters interdisciplinary collaboration, bringing together experts from data science, geography, urban planning, and environmental science. This collaborative approach enhances innovation and drives comprehensive solutions to complex challenges. As we embrace this evolution, it is crucial for professionals across industries to stay informed and adapt to these advancements. The integration of Machine Learning into GIS is not merely an enhancement; it is a fundamental shift in how we interact with and understand our world. Let's connect and discuss how we can leverage this powerful combination for a better future! 💡✨ Found value? Follow Rene Parekh Let’s start a conversation and mention your viewpoints in the comments. Reposting means a lot. #MachineLearning #GIS #DataScience #Sustainability #Innovation #UrbanPlanning #PredictiveAnalytics

Discover why we use H3 hexagonal grids for data analysis and visualization at Kontur! 🌐✨ In our blog post https://lnkd.in/d4V-EkHG, we talk about the power of hexagonal grids and their application in the Kontur Atlas Platform. If you've seen our Disaster Ninja maps, you've noticed the hexagons – but have you ever wondered why we use them? ✨Efficient Data Representation: 🌐Uniform Distance: Hexagons ensure that the distance between the center of each hexagon and its neighbors is consistent, making spatial queries more straightforward and accurate. 🌐Low Perimeter-to-Area Ratio: With their compact shape, hexagons have a lower perimeter relative to their area compared to squares or triangles. This reduces sampling bias and minimizes edge effects, leading to more reliable data analysis. 🌐Simplified Curves: The geometry of hexagons allows for a more natural representation of curved real-world objects such as roads and rivers, enhancing the accuracy of spatial visualizations. ✨Versatility in Application: 🌐Comparative Analysis: Hexagonal grids are perfect for comparing a wide range of datasets, including social statistics and natural indicators, across different regions. 🌐Seamless Aggregation: They enable smooth data aggregation and analysis, facilitating comprehensive insights into diverse data types. ✨Advantages over Other Grids: 🌐Minimized Distortion: Hexagons reduce distortion when mapping large areas, unlike squares and triangles, making them ideal for global-scale analyses. 🌐Optimal for Raster Data and Machine Learning: The structure of hexagonal grids makes them better suited for storing raster data and enhancing machine learning applications. 📊 Whether you're a data enthusiast or a geospatial expert, our blog offers valuable insights into the innovative use of hexagonal grids.https://https://lnkd.in/d4V-EkHG #DataVisualization #GeospatialAnalysis #HexagonalGrids