Interactive Data Visualization of Pakistan's Cities with Plotly

One thing I’ve realized while working on real datasets: EDA is not just about plots. It’s about asking the right questions. Over the past few days, I’ve been analyzing different features from an AI Models dataset — starting with individual columns like intelligence index and price. At first, it felt simple. Just visualize and move on. But the deeper I went, the more I noticed: • Every column tells a different story • Distributions reveal hidden patterns • Even a single feature can raise multiple questions I also realized that: You don’t truly understand data until you analyze it from multiple angles Now moving towards understanding relationships between variables — which is where things get even more interesting. #DataScience #EDA #LearningInPublic #Python #Analytics #dataanalysis

Day 11 of My AI Journey 🚀 Today I started working with data structures in Python. Covered: 👉 Lists and how to store multiple values 👉 Iterating over data using loops 👉 Basic operations like adding, removing, and accessing elements What I worked on: 👉 Built small programs using lists to manage and process data 👉 Practiced combining lists with loops and conditions Key takeaway: 👉 Real-world programs don’t deal with single values — they work with collections of data This step is helping me move closer to handling real datasets and preparing for AI concepts. #Python #AI #LearningInPublic #BuildInPublic

One rogue data point can completely skew your machine learning model. Check out this quick, visual guide breaking down the mechanics of Outlier Detection (IQR vs. Z-Score) and when you should cap vs. drop your data! #Part1 #DataScience #MachineLearning #DataCleaning #Python #DataEngineering #AI #TechEducation

Most ML time isn’t spent on modeling — it’s data cleaning. Tried skrub, and it genuinely simplifies the pipeline. You can go from raw data to a working model in minutes, especially for real-world tabular data. Worth checking out 👇 #skrub #MachineLearning #Python #DataScience #AI #MLOps #DataEngineering #ScikitLearn

Day 14 of My AI Journey 🚀 Today I focused on working with real data using file handling in Python. Covered: 👉 Reading and writing files 👉 Processing data from text/CSV files 👉 Combining file data with lists and dictionaries What I worked on: 👉 Built small scripts to read data, process it, and generate outputs 👉 Practiced handling real input instead of hardcoded values Key takeaway: 👉 Working with real data introduces new challenges and requires more structured thinking This step is helping me transition from practice problems to real-world data processing, which is essential for AI systems. #Python #AI #LearningInPublic #BuildInPublic

To make a decision tree, all data has to be numerical. We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values. Pandas has a map() method that takes a dictionary with information on how to convert the values. {'UK': 0, 'USA': 1, 'N': 2} Means convert the values 'UK' to 0, 'USA' to 1, and 'N' to 2. #MachineLearning #DataScience #Python #ArtificialIntelligence #AI #ScikitLearn #DataAnalysis #ML

✨ A New Beginning in My AI/ML Journey As part of the Industry Immersion Program by MeetMux, Day 3 marked my transition from setup to execution. 🔹 What I tackled today: Built a basic data pipeline using Python, NumPy, and Pandas — focusing on how data is processed, structured, and analyzed. 🔹 What I learned : The concept of vectorization in NumPy — instead of using loops, operations can be applied to entire datasets at once, making computations significantly faster. This is a core technique used in real-world AI systems. 🔹 My goal: To continue building a strong foundation in data handling and move towards implementing real-world machine learning models by the end of this week. 🔗 My Work (GitHub): https://lnkd.in/gQNYJ8ce #AI #MachineLearning #Python #NumPy #Pandas #IndustryImmersion #LearningInPublic #MeetMux

Built an AI Object Detector using Python! Feed any image → AI finds all objects,  draws boxes and shows confidence scores. Tested on a messy room image and detected: - Bed, couch, books, potted plant - All with 65-82% confidence Tech stack: - Python - HuggingFace Transformers - Facebook DETR model - Pillow + Matplotlib GitHub: https://lnkd.in/dj4PVi2D #Python #AI #ComputerVision #DeepLearning #Portfolio

Built a Machine Learning obesity prediction app in Python and Flask, reaching 76.6% accuracy. This project was a strong exercise in model building, evaluation, and deploying ML in a practical application. Repo: https://lnkd.in/dnq6kirn #MachineLearning #Python #Flask #DataScience #AI #ModelDeployment #HealthTech

Day 6 of becoming an AI/ML Engineer 💻 Today’s topic: Tuples & Sets ✔ Tuples → immutable data ✔ Sets → unique elements & operations Building strong programming basics every day! #Python #AI #ML #LearningInPublic #StudentLife