Python Ecommerce Customer Dataset EDA with Pandas and Matplotlib

🚀Project Showcase: Global YouTube Trending Videos Analysis (Multi-Country) In this project, I worked with real-world YouTube trending data from 10 different countries to build a complete end-to-end data cleaning and analysis pipeline using Python. 🔹 What I focused on: • Merging multi-country datasets into a single structured dataframe • Handling missing values, duplicates, and encoding issues • Validating data logically (likes, dislikes vs views) • Converting and standardizing date & data types • Detecting outliers using the IQR method • Mapping category IDs to readable category names using external data 📊 This project helped me understand how messy real data is handled in practical analytics scenarios and how data quality directly impacts insights. 🛠️ Tools used: Python | Pandas | Data Cleaning | Exploratory Data Analysis #DataAnalytics #Python #Pandas #EDA #DataCleaning #StudentProject #LearningByDoing #oasisinfobyte Oasis Infobyte

Project Objective: Analyzed 500+ bank customer records using Python to predict customer churn and identify key factors influencing customer churn. What this project covers: • Data preprocessing and exploratory data analysis (EDA) • Feature engineering and selection • Machine learning model training, prediction, and performance evaluation Tools used: Python | Pandas | NumPy | Matplotlib | Scikit-learn |Jupyter Notebook GitHub: 🔗https://lnkd.in/eBxzqe3Y #Python #DataScience #EDA #GitHub #LearningInPublic

Implemented advanced Data Visualization techniques under Code Alpha, transforming raw datasets into insight-driven visual representations. Designed line charts, bar graphs, pie charts, box plots, and scatter plots to evaluate sales trends, category performance, regional distribution, and student performance correlations using Matplotlib and Seaborn. 🔗 GitHub Repository: https://lnkd.in/gUCDR_ST � #CodeAlpha #DataVisualization #Python #Matplotlib #Seaborn #DataStorytelling

📊 Python Data Visualization Cheat Sheet Data tells a story — visualization is how we make it speak. This cheat sheet brings together the most-used plots from Matplotlib and Seaborn, all in one place for quick reference and daily practice. From line plots and bar charts to heatmaps and KDEs, these are the visuals every data analyst and data scientist should feel comfortable with. Simple concepts, strong foundations. 🚀 Save it, revisit it, and keep building clarity through visuals. #Python #DataVisualization #Matplotlib #Seaborn #DataScience #DataAnalytics #EDA #LearningInPublic #TechSkills #Consistency

I’m excited to share my latest data analysis project where I focused on visualizing data to uncover meaningful insights 📊 The objective was to create clear and effective bar charts / histograms to understand the distribution of categorical or continuous variables using Python. 📂 Check out the repository here: 🔗 https://lnkd.in/dJQ-y2m7 💡 What I worked on: Data cleaning and preprocessing Exploratory Data Analysis (EDA) Building visualizations using Pandas and Matplotlib Interpreting patterns from the data This project helped me strengthen my fundamentals in data visualization and analytical thinking. Looking forward to building more data-driven solutions! 👨💻 #DataScience #Python #EDA #DataVisualization #GitHub #MachineLearning #LearningJourney #ProdigyInfotech DS TASK 01

🧹 Data preprocessing matters more than we think. Before any model or insight, data needs work—a lot of it. Up to 80% of a data scientist’s time goes into cleaning messy data: missing values, duplicates, wrong formats, and inconsistencies . Tools like Python & Pandas make this easier with functions to detect, remove, and intelligently fill missing values—but the real skill is knowing what to fix and how. Better data = better decisions. Always. #DataScience #DataCleaning #Python #Pandas #MachineLearning #Analytics

Tools & technologies I used in my Mental Health Data Analytics project: 🔹 Python 🔹 Pandas for data cleaning & analysis 🔹 API integration for real-time data 🔹 Matplotlib/Excel for visualization Combining these tools helped me turn raw data into meaningful insights. #Python #Pandas #API #DataAnalytics #LearningJourney

Project Objective : Analyzed pizza order data using Python to predict pizza prices based on size, diameter, toppings, and extra options. The project focuses on understanding price-driving factors and building a prediction model. What this project covers : • Data cleaning and preprocessing • Exploratory Data Analysis (EDA) • Feature engineering • Price prediction using machine learning • Model evaluation Tools used : Python | Pandas | NumPy | Matplotlib | Jupyter Notebook GitHub : https://lnkd.in/gKr_JRWB #Python #MachineLearning #DataScience #EDA #StudentProject #LearningInPublic #GitHub