Titanic Dataset EDA with Python

📈 Project 2: Titanic Dataset – Exploratory Data Analysis (EDA) In this project, I explored patterns and insights from the Titanic dataset using visualizations. Insights Extracted: --Survival rate by gender and passenger class --Age distribution of survivors vs non-survivors --Impact of fare and family size on survival Tools Used: Python, Pandas, Matplotlib, Seaborn EDA helped me understand how data tells stories when visualized properly. #EDA #DataVisualization #Python #Seaborn #IncodeVision IncodeVision

🎥 Project Demo | Student Performance Prediction Here’s a short walkthrough of my Python project where I analyzed student performance data. 🔹 Loaded and analyzed the dataset using Pandas   🔹 Created a new feature (final score)   🔹 Visualized data using Matplotlib & Seaborn   🔹 Used histograms and correlation heatmaps for insights  This project helped me understand Exploratory Data Analysis (EDA) and data visualization concepts in a practical way. 📌 Tools: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook  Open to feedback and learning opportunities 🚀 #Python #DataAnalysis #EDA #MachineLearning #StudentProject #LearningByDoing

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

Learning NumPy – Array Slicing Today I practiced 1D & 2D array slicing in NumPy. Slicing helps us extract required rows and columns efficiently from large datasets. Example: array[row_index, column_slice] 🚀 Small concepts like slicing play a big role in Data Science & ML. #NumPy #Python #DataScience #LearningJourney #BCAStudent

Today I explored some common NumPy operations in Python 🐍 NumPy makes working with numerical data fast and efficient. Understanding its core operations is essential for data analysis and machine learning. Some important operations I learned: 🔹 Reshape – change array dimensions 🔹 Transpose – swap rows and columns 🔹 Sum – calculate total values 🔹 Mean – find average 🔹 Sort – arrange data 🔹 Max / Min – find extreme values These operations help transform raw data into meaningful insights. Still learning step by step, but enjoying the process of building strong foundations in data science 🚀 #Python #NumPy #DataScience #MachineLearning #LearningInPublic #100DaysOfCode #CareerSwitch

🐍 Python dominates data science in 2026, but success isn't just about knowing the language—it's about mastering the RIGHT libraries. After working with countless datasets and models, I've identified the 5 essential Python libraries every data scientist needs in their toolkit: 📊 Pandas - Data manipulation powerhouse 🔢 NumPy - Numerical computing foundation   📈 Matplotlib/Seaborn - Visualization storytelling 🤖 Scikit-learn - Machine learning workhorse 🚀 Polars - The speed game-changer 💡 Pro tip: Don't just learn syntax—understand WHEN to use each tool. What's YOUR essential Python library? 👇 #DataScience #Python #MachineLearning #DataAnalytics #AI #DataScientist #PythonProgramming #Analytics

From simulation to insight 📊 This visualization shows parametric estimation in action: generating data from a normal distribution, estimating mean and standard deviation, and validating the theoretical PDF against empirical data. A simple example, but a powerful reminder of how statistics, probability, and code come together to turn raw data into understanding. Data science is not just models—it’s foundations done right. #Python #DataScience

Here is my first Machine Learning model where I used a dummy dataset to predict salary using Linear Regression. The model achieved a prediction accuracy of 95.58% with exploratory data analysis (EDA) implemented in Python and Jupyter Notebook. #Python #JupyterNotebook #Pandas #Matplotlib #MachineLearning

🚀 Turning Student Data into Insights with ML! Analyzed how study hours and attendance affect exam performance 📊 Visualized trends and correlations, then applied an ML Linear Regression model using Python, Pandas, and Scikit-learn to predict student scores. This project demonstrates the workflow from raw data to ML predictions, combining data analysis, visualization, and model evaluation. Check out the code and notebook here: https://lnkd.in/g6kc3-QQ #MachineLearning #Python #DataScience #LinearRegression #DataVisualization #MLProjects #DataAnalysis

Exploratory Data Analysis (EDA) with Pandas - Cheat Sheet If you work with data in Python, this Pandas EDA cheat sheet is a handy reference 📊🐍 It covers:  • Data loading & inspection  • Cleaning & transformation  • Visualization basics Perfect for quick lookups while exploring datasets or revising core Pandas workflows. Feel free to save, share, or use it as a daily reference 🚀 #DataScience #Python #Pandas #EDA #MachineLearning #Analytics #DataAnalysis #LearningInPublic