Adam Sullivan’s Post

🔥🔥 Data Science in Healthcare: How it helps in Care 💥 Recognizing the upcoming problems is always good when it comes to healthcare. Mainly because no individual is hundred percent safe, irrespective of his or her age, gender, demography or ethnicity. Be it illnesses, accidents, genetic disorders or mutations, there are enough reasons with which every other person struggles at some stage of his or her life. 👉 Click here for more info: https://bit.ly/2QpsvLx 📌 If you are interested to learn more about how to start your career in Data Science, then visit: https://bit.ly/3qrYB5G Credit: Zarantech Blog ✍ ✍ COMMENT "Yes" if you like this post and want me to post more content like this. #datascience #datascientist #datascientists #dataanalysts #dataanalytics #python #artificialintelligence #machinelearning #ml #ai #deeplearning #r #onlinelearning #corporatetraining #learndatascience #datasciencejobs #interviewquestions #career #zarantech

🔥🔥 Data Science in Healthcare: How it helps in Care 💥 Recognizing the upcoming problems is always good when it comes to healthcare. Mainly because no individual is hundred percent safe, irrespective of his or her age, gender, demography or ethnicity. Be it illnesses, accidents, genetic disorders or mutations, there are enough reasons with which every other person struggles at some stage of his or her life. 👉 Click here for more info: https://bit.ly/2QpsvLx 📌 If you are interested to learn more about how to start your career in Data Science, then visit: https://bit.ly/3qrYB5G Credit: Zarantech Blog ✍ ✍ COMMENT "Yes" if you like this post and want me to post more content like this. #datascience #datascientist #datascientists #dataanalysts #dataanalytics #python #artificialintelligence #machinelearning #ml #ai #deeplearning #r #onlinelearning #corporatetraining #learndatascience #datasciencejobs #interviewquestions #career #zarantech

🔥🔥 Data Science in Healthcare: How it helps in Care 💥 Recognizing the upcoming problems is always good when it comes to healthcare. Mainly because no individual is hundred percent safe, irrespective of his or her age, gender, demography or ethnicity. Be it illnesses, accidents, genetic disorders or mutations, there are enough reasons with which every other person struggles at some stage of his or her life. 👉 Click here for more info: https://bit.ly/2QpsvLx 📌 If you are interested to learn more about how to start your career in Data Science, then visit: https://bit.ly/3qrYB5G Credit: Zarantech Blog ✍ ✍ COMMENT "Yes" if you like this post and want me to post more content like this. #datascience #datascientist #datascientists #dataanalysts #dataanalytics #python #artificialintelligence #machinelearning #ml #ai #deeplearning #r #onlinelearning #corporatetraining #learndatascience #datasciencejobs #interviewquestions #career #zarantech

🔥🔥 Data Science in Healthcare: How it helps in Care 💥 Recognizing the upcoming problems is always good when it comes to healthcare. Mainly because no individual is hundred percent safe, irrespective of his or her age, gender, demography or ethnicity. Be it illnesses, accidents, genetic disorders or mutations, there are enough reasons with which every other person struggles at some stage of his or her life. 👉 Click here for more info: https://bit.ly/2QpsvLx 📌 If you are interested to learn more about how to start your career in Data Science, then visit: https://bit.ly/3qrYB5G Credit: Zarantech Blog ✍ ✍ COMMENT "Yes" if you like this post and want me to post more content like this. #datascience #datascientist #datascientists #dataanalysts #dataanalytics #python #artificialintelligence #machinelearning #ml #ai #deeplearning #r #onlinelearning #corporatetraining #learndatascience #datasciencejobs #interviewquestions #career #zarantech

ML Engineer https://ift.tt/ESzMv7U via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 February 07, 2023 at 09:29AM . . . #dataengineer #datascience #python #datascientist #data #dataengineering #dataanalytics #bigdata #cat #machinelearning #dataanalysis #pythonprogramming #artificialintelligence #hadoop #devops #deeplearning...

Ready to embark on a thrilling career switch in 2024? Dive into the world of AI engineering, data science, analysis, and engineering! Unlock limitless opportunities in the realm of data-driven innovation. Let's shape the future together! #CareerSwitch #AI #DataScience #DataAnalysis #DataEngineering #NewBeginnings #dataanalyst #DataMites #aiengineer #datascientist

👋 Hello, LinkedIn Community! If you're eager to make a positive impact on healthcare using your data skills, this post is for you! In the intricate field of Biomedical Science, where cutting-edge research and patient care converge, the fusion of advanced SQL and Python optimization emerges as a potent catalyst for progress. 🧬🔬 Let's dive deep into how these tools can revolutionize data analysis and decision-making in this critical domain. 🔍 Navigating Complex Data Landscapes: Imagine you're working with voluminous datasets containing genomic sequences, patient phenotypes, and drug interactions. Extracting meaningful patterns and insights from this vast sea of data requires not just proficiency, but a precisely coordinated blend of technical expertise. 📊 Mastering Advanced SQL Dynamics: Consider a scenario where you're conducting research on genetic markers associated with a rare disease. By wielding advanced SQL techniques like indexing and query optimization, you can swiftly retrieve relevant genetic data from massive databases. This acceleration in data access equips you to identify potential genetic triggers for diseases and expedite the development of targeted therapies. 🐍 Python's Strategic Impact: Shift your focus to personalized medicine. Python's prowess in algorithm optimization can be pivotal in analyzing diverse patient profiles, selecting optimal treatments, and predicting individual responses. As you streamline these processes, you empower clinicians to provide tailored healthcare interventions, optimizing patient outcomes. 🔬 Illustrative Scenario in Biomedical Science: Imagine analyzing multi-omics data to uncover biomarkers for early cancer detection. By leveraging advanced SQL queries to filter datasets and Python scripts for data integration and machine learning, you accelerate biomarker discovery. This translates to quicker diagnoses and potentially life-saving interventions. 🔬 A Health Insurance Scenario: Imagine of analysing claims data to detect fraudulent activities. By leveraging advanced SQL queries to cross-reference claims with historical data and using Python scripts for anomaly detection, you expedite fraud identification. This translates to minimized losses and cost savings, benefiting both insurers and policyholders. 💡 The Significance of Optimization: Real-time Insights: Rapid data processing facilitates swift clinical decisions during critical situations. Precision Medicine: Optimized analytics pave the way for tailored treatments, considering individual genetic variations and medical histories. Scientific Innovation: A foundation of optimized data workflows opens avenues for ground-breaking discoveries, enhancing disease understanding and therapeutic breakthroughs. #healthcareanalytics #advancedsql #pythonoptimization #datadrivenhealthcare #innovationinhealthcare #healthtech #datadrivendecisions

A simple function for performing most biomedical / clinical NLP entity extraction tasks: The nature of my role throughout clinical, healthcare, biotech has me reviewing a lot of unstructured data (read: raw text). It has a the double-whammy of: 1) "it's a lot of biomedical/clinical jargon" 2) "it's a lot of text to sift through". How do I supercharge myself? (I challenge anyone to come close to my speed when it comes to medical content reviews 😂, I am equipped with LLM and data viz skillsets!) As much as organizations are debating "how do we use it! Let's hire a prompt engineer!", personally, I often find these tools as tools, (not really viable "products" to build your next startup) to enhance existing data scientist or data analyst productivity. Here's a simple example python function that calls OpenAI API, returning parseable JSON string. Basically a crappy API built to parse text, but effectively if you just need to do it once. You can potentially extend to make a skeleton proof-of-concept to pitch for the next data project showcase, or just simply use in your daily function. This is the type of work that often ends up in the hands of data vendors for healthcare organizations tagged with "AI-vendor". Just leverage your own programmers! P.S. For the knowledge graph crowd: I am aware old, outdated terminologies are often recommended (sometimes made-up), so take that element with grain of salt. https://lnkd.in/edxMkJUU

🧠 Exciting News! 📊 I'm thrilled to share my latest Data Science project - "Brain Tumor Detection"! 🌟 In this cutting-edge project, I've harnessed the power of Data Science and Machine Learning using Python, with a toolkit that includes essential libraries like NumPy, pandas, Matplotlib, scikit-learn, and more. 🔍 Detecting brain tumors early is critical for saving lives. By analyzing medical imaging data, I've developed a robust model that can identify brain tumors with remarkable accuracy. Here's a glimpse of what I've achieved: 📈 Data Preprocessing: I've cleaned and transformed complex medical data into a format suitable for training our model. This involved handling missing values, standardizing features, and preparing the dataset for analysis. 🧪 Model Building: Leveraging scikit-learn, I've split our data into training and testing sets, then trained a state-of-the-art machine learning model. This model has learned to distinguish between normal brain scans and those with tumors. 📊 Evaluation: Using accuracy metrics, I've rigorously evaluated the model's performance. The results are promising, showcasing its ability to make accurate predictions. 💡 Why is this project important? Early detection of brain tumors can significantly increase a patient's chances of recovery. Our model could assist medical professionals in identifying potential cases faster and more accurately. I'm excited to contribute to the healthcare field through Data Science. Stay tuned for more updates on this project, and feel free to reach out if you'd like to learn more or collaborate on similar initiatives! 🚀 Git Hub: https://lnkd.in/dc2V3bff #DataScience #MachineLearning #BrainTumorDetection #RetailAnalytics #SalesPrediction #InternshipComplete Arbre Creations Console Flare Genpact HCLTech Celebal Technologies EXL Google #DataScienceFoundations #GreatLearning #LifelongLearner #Optum #Pandas #ConsoleFlare #Python #ArbreCreations #DataAnalysis #DataAnalytics #SQLCertification 

Python Machine Learning Project - 20/30 🐍 Heart Disease Prediction 🏥 💟 Purpose Cardiovascular diseases are a leading cause of mortality globally. Early detection and prediction of heart disease can significantly improve patient outcomes. This project intends to leverage machine learning techniques to create a predictive model that can assist in identifying individuals at risk of heart disease. The dataset used for this project contains information about individuals' health, lifestyle, and medical history. 💟 Data Preprocessing 📍 Uploading and Loading Data The project starts with uploading a dataset, namely "heart_disease.csv," containing relevant information. The Pandas library is used for data manipulation and analysis. The dataset is loaded into a Pandas DataFrame for further processing. 📍 Handling Missing Values Data quality is crucial for accurate predictions. Missing values are handled by removing rows with null values, ensuring a clean dataset for analysis. 📍 Feature Engineering Some features in the dataset are transformed for better compatibility with machine learning algorithms. For example, the 'Gender' column is encoded numerically, replacing 'Male' with 1 and 'Female' with 0. 📍 Handling Categorical Variables Certain categorical variables, such as 'prevalentStroke' and 'Heart_Stroke,' are converted into numerical format (0 or 1) to facilitate model training. 📍 Addressing Class Imbalance Imbalanced classes, where one class significantly outnumbers the other, can lead to biased models. The Synthetic Minority Over-sampling Technique (SMOTE) is employed to balance the distribution of the target variable ('Heart_Stroke'). 💟 Model Training 📍 Data Splitting The dataset is divided into training and testing sets to evaluate the model's performance. The training set is used to train the logistic regression model, while the testing set assesses its accuracy on unseen data. 📍 Logistic Regression Model Logistic regression is chosen as the machine learning algorithm for its simplicity and interpretability. It's well-suited for binary classification problems, such as predicting the presence or absence of heart disease. 📍 Model Evaluation The trained model's performance is evaluated using the test set. The accuracy score indicates the proportion of correctly predicted instances. 📍 Model Deployment Once satisfied with the model's performance, it is saved using the Pickle library for future use. The saved model file, "Heart_disease_model.pk1," can be deployed in various applications to predict heart disease based on input data. ↪ Dataset ✔ https://lnkd.in/gaFTSiez ↪ GitHub ✔ https://lnkd.in/gaFTSiez #python #machinelearning #30daysofcode #30daysofpython