Boston House Price Predictor Data Distribution

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

Check the statistics 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 

🚀 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

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 

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

"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

#Day2 of my #30dayscodingchallenge What I learned today: • Variables and data types in Python • Taking user input • Basic arithmetic operations Mini Project: I built a simple calculator using Python that can perform addition, subtraction, multiplication, and division. Key takeaway: Every big skill starts with small basics. Understanding fundamentals clearly is the real game changer. I am committed to showing up every single day and improving step by step. If you are also learning or planning to start, let’s connect and grow together #30daysofcode #pythondeveloper #codingjourney #learnpublic #fullstackdeveloper

Understanding how to access data efficiently Day 34 at Luminar Technolab Worked on NumPy indexing and slicing forward indexing, backward indexing, and slicing in both 1D and 2D arrays. Learning how to navigate data more effectively step by step. #Python #NumPy #DataHandling #LearningJourney #Consistency

🔁 Exploring Sorting Algorithms in Python Today I practiced two fundamental sorting techniques: ✅ Bubble Sort ✅ Selection Sort 💡 Key Learnings: * Bubble Sort repeatedly swaps adjacent elements to push larger elements to the end * Selection Sort selects the minimum element and places it in the correct position * Understanding time complexity becomes clearer when you count operations manually #Python #DataStructures #Algorithms #CodingJourney #100DaysOfCode #LearningInPublic