Boston House Price Predictor Data Statistics

Check univariate distribution of the predictor data that will be created for machine learning modeling for determining house prices in Boston using Python. Data source: US Services. #python #datascience #machinelearning #house #pricing #houseprice

Check bivariate correlation matrix of the predictor data that will be created for machine learning modeling for determining house prices in Boston using Python. Data source: US Services. #python #datascience #machinelearning #house #pricing #houseprice

Visualization of decline curve analysis of a gas well that will be used as material for creating a machine learning model using Python. Data source: Julio Cesar. #python #datascience #machinelearning #petroleumengineer #production #subsurface 

New data for experimental application of machine learning methods. Production data from a gass well. Data source: Julio Cesar. #python #datascience #machinelearning #petroleumengineer #production #subsurface 

🚀 Text Generation Project | Prodigy InfoTech Developed a machine learning-based text generator using Python. The system processes input queries and returns the most relevant output 🔧 Tech Stack: Python | pandas | scikit-learn | NumPy 📈 Gained hands-on experience in: * Text preprocessing * Feature extraction * Similarity-based prediction Looking forward to building more AI-powered applications. #ProdigyInfoTech #AIProjects #PythonDeveloper #TechJourney

Task 3(Intermediate Level): Clustering Analysis (K Means) Description: Implement K-Means clustering to group similar data points together based on feature similarities. Tools: Python, scikit-learn, matplotlib, seaborn I standardized the dataset (using StandardScaler). I applied K-Means clustering and determined the optimal number of clusters using the elbow method. I visualized the clusters using 2D scatter plots. #CodvedaAchievements #CodvedaProjects #CodvedaJourney #CodvedaExperience #FutureWithCodveda Codveda Technologies

📊 Permutation & Combination in Data Analysis using Python Today, I explored the concepts of Permutation and Combination and applied them in data analysis using Python. Understanding these statistical techniques helps in solving problems related to arrangement, selection, and probability. By using Python, I was able to efficiently implement these concepts for analyzing different data scenarios and improving problem-solving skills. This practice enhanced my knowledge in combinatorics and its real-world application in data analysis and decision-making. Continuing my journey in learning advanced data concepts and applying them practically! 🚀 #DataAnalysis #Python #Statistics #Permutation #Combination #Combinatorics #DataScience #ProblemSolving #LearningJourney #DataAnalytics

"Kelly's Coefficient of Skewness" self made statistical function in python. Types of Skewness are Negatively Skewed, Symmectric (Not Skewed), Positively Skewed. #python #DataScience #statistics #skewness #distribution #coefficient #negative #positive #left #right #kelly #symmectric #data

Probability, linear algebra, calculus, matrices, Python, machine learning… all these things slowly coming together as I learn quantitative finance. Built and tested in Jupyter, here are 3 models I’ve been exploring lately: – Hidden Markov Model – Hierarchical Risk Parity – Sequential Monte Carlo Exploring more every day.

Start strong: XGBoost 3.2.1 delivers further speed improvements and categorical handling updates for predictive modeling. Changes: https://lnkd.in/gK4A79-H In ML work, these boost efficiency on larger datasets. Following XGBoost patches? Views? #XGBoost #MachineLearning #Python #DataScience #AIProgress