Before statistics. Before machine learning. Before dashboards. There is EDA. 📉 Come, let’s revise EDA concepts together. If you’re learning data analysis — Don’t skip EDA. It’s where intuition meets logic. #EDA #DataAnalysis #Statistics #LearningInPublic #Python #DataAnalytics #AnalyticsThinking

🚀 Data Analysis Project – 2/3 🍷 Wine Quality Prediction using Classification Models Predicted wine quality using chemical properties and compared Random Forest, SGD, and SVC models. #DataAnalysis #MachineLearning #Python #DataScience #Projects #Learning #OasisInfobyte

📊 Seaborn makes data easy to understand, not just easy to plot. In Python, Seaborn stands out because it focuses on clarity over complexity. ✔ Clean visuals by default ✔ Built for statistical insights ✔ Works seamlessly with Pandas ✔ Perfect for analytics, ML, and data engineering Good visuals don’t just look nice — they drive better decisions. If you work with data, Seaborn is a skill worth mastering. #Python #Seaborn #DataVisualization #DataAnalytics #DataScience

𝗖𝗿𝗲𝗮𝘁𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀! 🖥️ Machine learning models lack explainability, hence making their predictions difficult to interpret. This can be a significant challenge in regulated industries, where black box implementations are unacceptable. explainerdashboard is a Python library that helps you understand machine learning models by providing an interactive dashboard. The library supports various approaches, including SHAP values, permutation importances and dependence plots. Check the link below for more information, and make sure to follow me for regular data science content! 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗶𝘁𝗲: https://lnkd.in/dfFkMGjH 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

Today I worked on skewness in data analysis and explored: ➕ Positively skewed data ➖ Negatively skewed data 🔔 Normal distribution Along with this, I implemented Mean, Median, and Mode using Python to understand how these measures behave under different distributions. This practice helped me clearly see the relationship between data shape and statistical measures. Learning by doing, one concept at a time 🚀 #DataScience #Statistics #Skewness #Python #DataAnalysis #LearningJourney #Analytics

📈 Exploratory Data Analysis: Understanding Data Before Modeling After handling missing values, I dived deeper into EDA by analyzing: ✔ Feature distributions & variability 📊 ✔ Outliers & extreme values 🚨 ✔ Relationships & correlations between variables 🔗 ✔ Feature importance & data patterns 🎯 This process helped me clearly understand how each feature behaves and influences the target variable, making downstream feature engineering ⚙️ and model building 🤖 more effective. Strong EDA = Better decisions 🧠 | Cleaner pipelines 🛠️ | More reliable models 🚀Building consistency in Python , EDA 🔍, and end-to-end data workflows 📈 through continuous hands-on practice 💻 #EDA #ExploratoryDataAnalysis #DataScienceJourney #Python #MachineLearning #FeatureEngineering #FeatureSelection #DataAnalytics #DataPreprocessing #ModelBuilding #LearningByDoing #AnalyticsLife

Learning how to turn data into business insights. Today, I analyzed a sales dataset using Python & Pandas: Calculated total sales Sorted products by performance Measured percentage contribution of each product Understanding not just the “what”, but the “why” behind the numbers. #Python #Pandas #DataAnalytics #LearningByDoing #AspiringDataAnalyst

✅ Week 2 Complete | AI Learning Journey This week I focused on Python for data handling. 📌 Covered: • NumPy arrays & operations • Pandas DataFrames • Data cleaning & filtering • Aggregation and basic analysis 🧠 Key takeaway: Real-world data is messy, and cleaning it properly is a skill. Next up: 📈 Data visualization & SQL basics. #DataScience #Python #NumPy #Pandas #AIJourney

Explored the Titanic dataset using a structured EDA approach—starting from data loading and profiling to univariate and bivariate analysis. Focused on data quality checks, feature engineering, and extracting meaningful insights before modeling. A great exercise in understanding how much story the data tells even before machine learning. Guided by Harshvardhan Singh #DataAnalytics #EDA #Python #DataProfiling #FeatureEngineering #LearningByDoing

Day 14 – Python & Machine Learning Learning Journey Today was all about revision + practice 📊🐍 🔹 Revised core Python & ML concepts 🔹 Worked on California Housing Dataset 🔹 Built & trained 5 Machine Learning models, including Linear Regression 🔹 Practiced House Price Prediction Concepts Revised & Applied: Training Data vs Testing Data Features & Labels ✔️ Train–Test Split ✔️ Prediction Workflow ✔️ Underfitting vs Overfitting ✔️ Exploratory Data Analysis (EDA) Also revised EDA concepts using the Titanic Dataset to better understand data patterns, distributions, and missing values before model training. 💡 Key Learning: A strong model doesn’t start with algorithms — it starts with understanding the data. Excited to move forward and apply these concepts to more real-world datasets Consistency is the key #Python #MachineLearning #DataScience #LearningJourney #EDA #LinearRegression #CaliforniaHousing #TitanicDataset #AI #100DaysOfCode #Day14