Dates often look simple in a dataset. But to a machine, they are just strings until we tell it otherwise. In this project, I focused on date parsing and time-based feature extraction using Python and Pandas. Working with earthquake and landslide datasets, I explored how to: • Identify incorrect data types in date columns • Convert string-based dates into proper datetime format • Extract useful components such as the day of the month • Validate parsing results through distribution visualization The objective wasn’t just converting dates. It was understanding how correct data types enable time-based analysis, prevent errors, and make datasets usable for modeling and exploration. Sometimes, meaningful insights begin with something as simple as telling the system that a column is actually a date. #DataScienceJourney #DataCleaning #Python #Pandas #DataAnalysis #MachineLearning #LearningJourney

📅 Day 9/30 — NumPy Indexing & Slicing Continuing my 30-day journey into data science, today I explored how to efficiently access and manipulate data using NumPy arrays. What I worked on today: 🔢 Accessing elements using indexing (including negative indexing) ✂️ Extracting data using array slicing 🔁 Selecting elements using step slicing 🎯 Using index arrays to pick specific elements 🧠 Applying boolean masking to filter data based on conditions It was interesting to see how NumPy provides powerful ways to quickly access, modify, and filter data, which is very useful when working with large datasets. ➡️ Next step: exploring more advanced NumPy operations and applying them to real-world data. #LearningInPublic #Python #DataScience #NumPy #30DaysOfLearning #ProgrammingJourney

𝐂𝐫𝐚𝐜𝐤𝐞𝐝 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 𝐨𝐧 𝐇𝐨𝐮𝐬𝐞 𝐏𝐫𝐢𝐜𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧! I just wrapped up a deep dive into Predictive Modeling using the classic California Housing Dataset. Beyond just fitting a model, I focused on clean data visualization and resolving distribution skews to ensure high-performance results. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:  𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: Linear Regression  𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Modernized EDA using Seaborn histplot & probplot  𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: Python, Scikit-learn, Pandas, NumPy  𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Managed via a clean, professional GitHub workflow. Check out the full implementation and clean repository in first comment below! #MachineLearning #DataScience #AIEngineering #Python #GitHub #LinearRegression #HousePricePrediction

🚀 Day 4 – Data Science Learning Journey Today’s session reinforced key statistical fundamentals, strengthening concepts that form the backbone of data analysis. Along with theory, I explored Seaborn, a powerful Python library for statistical data visualization. Using the tips.csv dataset, I performed several visualizations to understand patterns, relationships, and distributions in the data. It’s fascinating to see how statistics and visualization together turn raw data into meaningful insights. Looking forward to learning more as the journey continues. 📊 #DataScience #Statistics #Seaborn #Python #DataVisualization #LearningJourney

🚀 Day 13 of Sharing My Data Science & Machine Learning Journey  Understanding the "Spread" – Measures of Dispersion Yesterday we found the center; today we find out how much the data deviates from it! 📊 In Data Science, knowing the average (Mean) isn't enough. You need to know if your data points are clustered closely together or scattered far apart. This is where Measures of Dispersion come in. #DataScience #MachineLearning #Statistics #MeasureOfDispersion #Python #LearningJourney

🚀 Machine Learning Project: Housing Price Prediction I recently built a Linear Regression model to predict house prices based on features such as area and number of bedrooms. 🔹 Tools Used: Python, Pandas, NumPy, Matplotlib, Scikit-learn 🔹 Steps: • Data preprocessing • Train-test split • Linear Regression model training • Model evaluation 📊 Visualized the relationship between house area and price using regression plots. This project helped me strengthen my understanding of regression models and data preprocessing. 🔗 GitHub: https://lnkd.in/dSe2YRzY Colob link :-- https://lnkd.in/ds52b_YY #DataScience #MachineLearning #Python #LinearRegression

Python Data Visualization Quick Guide V1.0 📊 What’s inside: • Distribution plots (Histogram, KDE, Box, Violin) • Categorical analysis (Bar, Count, Pie) • Relationship plots (Scatter, Regression, Bubble) • Time series visualizations (Line, Area) • Multivariate exploration (Heatmaps, Pairplots) • Hierarchical charts (Sunburst, Treemap) • Geographic maps with Plotly • Faceting and subplot layouts • A Visualization Selection Guide to help choose the right chart quickly 🔗 Notebook link: https://lnkd.in/daHNQpdq I’d love to hear your feedback and suggestions for improving it further. #Python #DataScience #DataVisualization #EDA #MachineLearning #Plotly #Seaborn #Matplotlib

Data isn't just 3D. Often, it’s 10-dimensional, 100-dimensional, or more. How do you find patterns when you can't even visualize the space? Enter Principal Component Analysis (PCA). In our latest video, Dr. Sindhu Ghanta demystifies PCA in 3 simple steps to help you collapse high-dimensional complexity into actionable insights: - The Geometric Intuition behind the best angles - The Math Under the Hood (simplified!) - Practical Pitfalls and when PCA actually fails Watch the full breakdown and grab the Python notebook to try it yourself! 👇 ▶️ Watch: https://lnkd.in/gdGkEw8r 👨💻 Code: https://lnkd.in/gUQmiDkp #MachineLearning #DataScience #PCA #Python #Schovia

🚀 Day 48 of My 90-Day Data Science Challenge Today I worked on Feature Selection Techniques. 📊 Business Question: How can we select the most important features to improve model performance? Feature selection helps remove irrelevant or redundant features and improves efficiency. Using Python & scikit-learn: • Applied SelectKBest • Used Correlation Analysis • Understood Feature Importance • Reduced dimensionality • Improved model performance 📈 Key Understanding: Not all features are useful — selecting the right ones improves accuracy and speed. 💡 Insight: Removing unnecessary features helps reduce overfitting. 🎯 Takeaway: Better features lead to better models. Day 48 complete ✅ Improving data quality 🚀 #DataScience #MachineLearning #FeatureSelection #Python #LearningInPublic #90DaysChallenge

🚀 Quick NumPy Revision + Assignment Completed While learning Data Science, I created these quick notes for NumPy to revise important concepts like: ✔ Creating NumPy arrays ✔ Understanding array dimensions (ndim) ✔ Reshaping arrays ✔ Random number generation ✔ Functions like zeros, eye, and linspace ✔ Array operations & indexing ✔ Mathematical operations on array ✔ Searching array These small notes help me revise NumPy faster while practicing Python for Data Science and Machine Learning. 📂 Assignment available on GitHub: https://lnkd.in/dX66epMw #Python #NumPy #DataScience #MachineLearning #LearningInPublic #100DaysOfCode