Payal Pattnaik’s Post

🔬 Data Visualization Expert | Unveiling Insights from Heart Health Data 📊 Excited to unveil the findings of my latest data visualization project, diving deep into the intricate realm of heart health data. Leveraging advanced data analytics techniques, I extracted invaluable insights from a comprehensive dataset, unraveling trends and patterns crucial for enhancing cardiovascular care. 🔍 Exploration: Through meticulous analysis, I scrutinized various dimensions of the dataset—patient demographics, medical history, lifestyle factors, and treatment outcomes. This thorough examination unveiled hidden correlations and dependencies, illuminating the complexities of heart health. 📈 Visualization: Armed with robust data visualization tools like Matplotlib and Seaborn, I transformed raw data into insightful charts, graphs, and interactive dashboards. These visual representations provided a comprehensive view of heart health dynamics, facilitating informed decision-making in healthcare. 💡 Key Insights: Amidst the wealth of discoveries, several pivotal insights emerged: Risk Factor Identification: The data highlighted key risk factors contributing to cardiovascular diseases, shedding light on preventive measures and intervention strategies. Treatment Effectiveness: Analysis of treatment outcomes revealed the efficacy of various interventions, guiding healthcare providers in optimizing patient care pathways. Impact of Lifestyle Choices: Lifestyle factors such as diet, exercise, and stress emerged as significant determinants of heart health, emphasizing the importance of holistic approaches to disease management. Demographic Disparities: Variations in disease prevalence and treatment outcomes across demographic groups underscored the need for targeted healthcare interventions tailored to specific populations. 🔍 Conclusions: Drawing from the insights gleaned, several strategic implications come to the fore: Personalized Healthcare: Tailoring treatment plans to individual patient profiles can enhance outcomes and mitigate the burden of cardiovascular diseases. Preventive Interventions: Investing in preventive healthcare measures and health promotion initiatives is essential for reducing the incidence of heart diseases and improving population health. Data-Driven Decision-Making: Harnessing data analytics and visualization tools enables healthcare stakeholders to make evidence-based decisions and drive continuous improvement in cardiovascular care delivery. Please find my visualisation project here: https://lnkd.in/gnDng3_n 🔗 Interested in exploring further insights or collaborating on similar projects? Let's connect! Feel free to reach out to delve deeper into heart health insights or discuss potential collaborations in healthcare analytics! #datascience #dataanalytics #datavisualisation #matplotlib #seaborn #plotly #pandas #healthcareanalytics

🚀 𝐔C𝐈 𝐇e𝐚r𝐭 𝐃i𝐬e𝐚s𝐞 𝐏r𝐞d𝐢c𝐭i𝐨n!🚀   I want to share my work with the UCI Heart Disease Prediction dataset. This project was a deep dive into the complications of Exploratory Data Analysis (EDA) and Machine Learning, culminating in the creation of an application that can predict heart disease conditions based on patient inputs.   🔍 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: 𝑪𝒐𝒎𝒑𝒓𝒆𝒉𝒆𝒏𝒔𝒊𝒗𝒆 𝑬𝑫𝑨: Started with a thorough exploratory analysis to understand the dataset, uncover patterns, and identify crucial factors affecting heart health. 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑰𝒎𝒑𝒍𝒆𝒎𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏: Used various algorithms to train models, and tuning it to achieve optimal performance. 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏: Transformed the model with the help of 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭 into a user-friendly app, making heart disease predictions accessible to non-experts. Users can enter specific health parameters to receive instant predictions.   🔗𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒍𝒊𝒏𝒌: https://lnkd.in/d-yYgMct 𝑨𝒑𝒑 𝒍𝒊𝒏𝒌: https://lnkd.in/dySeuNZ2   🎯 𝐆𝐨𝐚𝐥: This project was an academic exercise. The app aims to provide preliminary assessments, encouraging users to seek medical advice armed with information.   💡 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬: The journey of data science is teaching me the importance of data-driven decision-making, the power of machine learning in different aspects, and the challenges of translating complex models into user-friendly applications.   I am looking forward to receiving feedback from the community and exploring how this project can evolve. Whether you're a healthcare professional, a fellow data scientist, or someone passionate about technology's role in healthcare, I’d love to hear your thoughts!   #DataScience #MachineLearning #HealthcareInnovation #HeartDiseasePrediction #TechnologyForGood

Task 04 completed under Data Science domain @Prodigy Infotech Analyze and visualize sentiment patterns in social media data to understand public opinion and attitudes towards specific topics or brands. Understanding of these sentimental trends can inform decision-making process, marketing strategies, community engagement efforts both online and offline. #prodigy_infotech #self_learning #data_science Checkout Task-4 at Github: https://lnkd.in/gwJ-azTH

Data collection is an essential process to provide insights and support decision makers in their businesses and policy makers globally in making effective policies. There are two approaches to data collection: primary and secondary data. Primary data collection involves original data collected directly from the source, while secondary data uses existing data collected by someone else. The data collection instruments for primary data collection include surveys, questionnaires, experiments, observations, focus group discussions, and interviews. On the other hand, secondary data is available through publications, government institutions, social media, publicly available datasets, and more. check out this questionnaire I designed and developed: https://lnkd.in/dMU5R4rw.

🚀 Excited to Share My Latest Project! 🚀 I just completed an in-depth Exploratory Data Analysis (EDA) assignment on the Heart Disease Prediction Dataset. This project involved: 🔍 Investigating basic statistics such as patient counts and age distribution. 📊 Visualizing complex relationships using advanced methods like pair plots and t-SNE. 🔗 Performing multivariate analysis to understand how heart disease correlates with multiple factors. 🔧 Applying feature engineering techniques to create new, insightful features. This assignment has significantly enhanced my data analysis and machine learning skills. I'm excited to apply these techniques in real-world projects and contribute to data-driven solutions in healthcare. #DataScience #MachineLearning #HealthcareAnalytics #FeatureEngineering #DataVisualization #HeartDisease Here is my GitHub repo link: https://lnkd.in/g8GVsaN4

Hello Everyone.... 🚀 Excited to share my latest project challenge! 🚀 Task-03: 📊 Delving into the world of predictive analytics! 📊 In my recent endeavor with Prodigy, I embarked on the task of building a decision tree classifier to predict customer purchases based on demographic and behavioral data. 🛒🎯 🔍 Key Takeaways: Decision Tree Model Training: Utilized a Decision Tree Classifier to predict customer purchases based on demographic and behavioral data. Data Preparation: I meticulously prepared the dataset by defining features and converting categorical variables. Model Building: Leveraging the power of Decision Tree Classifier, I trained the model to predict customer behavior accurately. Evaluation: Rigorously assessing the model's accuracy and performance provided invaluable insights into its predictive capabilities. Visualization: Visualizing the decision tree offered a profound understanding of the factors influencing customer purchase decisions. Actionable Insights: Armed with these insights, I'm geared to formulate targeted marketing strategies tailored to customer behavior patterns. 💼💡 This project not only honed my skills in predictive analytics but also deepened my understanding of the intricate relationship between data and consumer behavior. 💡📈 #DataScience #PredictiveAnalytics #DecisionTree #MachineLearning #DataInsights #MarketingStrategy #DataDrivenDecisions #prodigyinfotech Check out my project here! https://lnkd.in/g5hkQQ2V

Project Showcase: Predicting Potential Customers for Telecom Wellness Plan This is the scenario:- As a Data Scientist at O2R2 Mobile, I had the exciting opportunity to leverage data analytics to drive business growth by predicting potential customers for our newly introduced Wellness Plan. Here's a summary of our approach and findings: 🔍 Objective: To predict which customers are more likely to purchase the newly introduced telecom plan, facilitating more efficient marketing strategies. 📝 Data Overview: Analyzed customer data including demographics, contact information, and interaction history. Explored the relationships between various features and plan purchases using Exploratory Data Analysis (EDA). 🛠️ Methodology: Data Cleaning: Addressed missing values and corrected errors in the dataset. Feature Engineering: Engineered new features and selected relevant ones for model training. Model Preparation: Employed Logistic Regression with Regularization and Deep Neural Network (DNN) techniques. Implemented data preprocessing pipelines and addressed class imbalance using RandomOverSampler. Model Training and Evaluation: Trained both Logistic Regression and DNN models on resampled data. Evaluated model performances using accuracy scores and classification reports. 🚀 Next Steps: Implement model predictions to optimize marketing expenditure and target potential customers more effectively. Continuously refine models with new data and feedback to improve prediction accuracy and business outcomes. 🔗 Link to GitHub Repository:

🌟 Task 4 Complete! 🌟 Analyzed and visualized sentiment patterns in social media data to understand public opinion and attitudes towards specific topics and brands. Huge thanks to PRASUNET COMPANY for this amazing opportunity! 🛠 Skills Gained: - Data Analysis - Sentiment Analysis - Data Visualization - Social Media Analytics Check out the project on GitHub: https://lnkd.in/gsgck4kS #DataScience #SentimentAnalysis #DataVisualization #SocialMediaAnalytics #Grateful #LearningJourney #Prasunet

📊 Cutting-Edge Diabetes Risk Prediction Analysis: A Comprehensive Overview Dear LinkedIn community, I am thrilled to share the outcomes of an in-depth analysis conducted on a dataset aimed at advancing early diabetes detection through sophisticated machine learning models. The study encapsulates a meticulous exploration of 520 data points across 17 variables, demonstrating our commitment to leveraging technology for healthcare enhancement. 📌 Dataset Insights: A meticulous examination of a dataset comprising 520 entries and 17 attributes, with a specific focus on predicting early signs of diabetes. Rigorous preprocessing ensured data integrity, addressing data types, and managing potential duplicates. 🤖 Model Training and Evaluation: Rigorous training of diverse classifiers such as RandomForest, GradientBoosting, XGBoost, and DecisionTree. Performance evaluation metrics include F1 Score, Accuracy, Confusion Matrix, and Precision-Recall Curve. ⚙️ Key Model Highlights: RandomForest Model: Initial exceptional F1 Score of 0.9984 underscores its robustness. Post hyperparameter tuning, sustained performance with an F1 Score of 0.9671 and an accuracy of 95.96%. GradientBoostingClassifier: Demonstrated consistent excellence both pre and post hyperparameter tuning, boasting F1 Scores of 0.9969. XGBoost Model: Impressive pre-tuning performance (F1 Score: 0.9953) saw a marginal dip post-tuning (F1 Score: 0.9378). DecisionTree Model: Post-tuning, displayed commendable performance with an F1 Score of 0.9360. 🎯 Strategic Recommendations: Deployment Strategy: Advocating the deployment of tuned RandomForest and GradientBoostingClassifier models for practical applications. Continuous Optimization: Emphasizing the imperative of continuous monitoring and retraining for sustained efficacy. Collaborative Synergy: Proposing synergy with healthcare professionals to enrich model accuracy through domain expertise. 🚀 The potential impact of these models in early diabetes detection is profound. Let us collectively pioneer a new era in healthcare through the convergence of artificial intelligence and medical science. #MachineLearning #HealthcareInnovation #DataScience #DiabetesPrediction #AIInHealthcare #CorporateHealth #AdvancedAnalytics

🎉 **Exciting Milestone Achieved!** 🎉 I'm excited to share my most recent machine learning project, where I developed a recommendation system for a mobile carrier. The goal was to analyze customer behavior and recommend the best new plan (Smart or Ultra) to encourage the adoption of their latest offerings. The model achieved an impressive accuracy of 82.58%, well above the 75% target! 📊 🔍 **Project Highlights:** - **Objective:** Recommend new mobile plans based on subscriber behavior. - **Data:** Analyzed monthly behavioral data including calls, minutes, messages, and internet usage. - **Approach:** Tested various models (DecisionTreeClassifier, RandomForestClassifier, LogisticRegression) and optimized hyperparameters. - **Best Model:** Random Forest Classifier with an accuracy of 82.58%. - **Results:**  - Achieved business objective with high accuracy.  - Validated effectiveness through sanity checks against baseline models. This project enhanced my skills in data analysis, model training, and hyperparameter optimization. It was a fantastic opportunity to apply my knowledge to a real-world problem and deliver actionable insights. 🔗 Check out the full project details here: https://lnkd.in/gkuFfYUh I'm now eager to bring these skills to a professional setting. If you or someone in your network is looking for a dedicated data scientist or analyst, let's connect! 📈📊 #DataScience #MachineLearning #RecommendationSystem #Megaline #RandomForest #DataAnalysis #JobSeeker #DataAnalyst #DataScientist