Moaz El-Morshdy’s Post

Exploring data tells powerful stories. Here’s a visualization of the Selling Price Distribution from my recent analysis. The histogram (with KDE) clearly shows a right-skewed distribution, where most vehicles are concentrated in the lower price range, while only a few fall into higher price brackets. Key Insights: • Majority of selling prices lie between 1–6 lakhs • A long tail indicates presence of high-value outliers • The distribution is not normal, which impacts modeling choices This kind of analysis is crucial before applying any machine learning model, as it helps in understanding data behavior and potential preprocessing needs. #DataScience #DataAnalysis #Python #MachineLearning #DataVisualization #LearningJourney

I’m excited to share my first-ever deployed machine learning project: a Heart Disease Prediction App. It’s been a rewarding challenge to move beyond notebooks and build a functional tool that is fun to use. The Tech Stack: Model: K-Nearest Neighbors (KNN) built with Scikit-Learn. Interface: Streamlit for the front-end and deployment. Data: End-to-end pipeline including cleaning and feature scaling. Try the app here: https://lnkd.in/gW8qFtmN #MachineLearning #DataScience #Python #Streamlit #FirstProject

🚀 Day 45 of My Learning Journey – NumPy Shape & Reshape Today, I explored how to work with array dimensions using NumPy, focusing on shape and reshape. 🔹 Key Learnings: ✔️ shape Helps to identify the dimensions of an array Example: (3, 2) → 3 rows and 2 columns ✔️ Modifying shape We can directly change the structure of an array Useful when reorganizing data ✔️ reshape() Creates a new array with a different shape Does NOT modify the original array Very helpful in data preprocessing 🔹 Hands-on Task Completed: Converted a list of 9 elements into a 3×3 matrix using NumPy. 💡 Takeaway: Understanding how to manipulate array dimensions is essential for data analysis, machine learning, and efficient problem-solving. 📌 Every small concept builds a stronger foundation! #Day45 #Python #NumPy #LearningJourney #DataScience #Coding #StudentLife

Inspired by Ron Kohavi and Evan Miller practical experimentation principles and tooling, I built a simple A/B Test Sample Size Calculator with Plotly Dash + statsmodels. Repo: https://lnkd.in/gkNvnkin Built quickly through vibe coding, but grounded in solid stats: fixed-horizon two-sample z-test for proportions, alpha/power controls, effect-size views, and runtime estimation. After years in experimentation, this automates a planning task I’ve repeated across various products/domains. An example of what experience + fast execution with AI assistance can produce. #ABTesting #Experimentation #ProductAnalytics #Growth #DataScience #OpenSource #Python #Plotly #Dash #Statsmodels #VibeCoding

I'm doing something a little different ----> I'm learning, practicing, and building all at the same time. The data came in as one messy array. Everything was a string ----> step counts, calories, mood, all jumbled together. Before I could analyze anything, I had to separate and convert each column manually: Python date, step_count, mood, calories, sleep, activity = data.T step_count = np.array(step_count, dtype='int') Took me a while to understand WHY this works. .T transposes the array ----> rows become columns, columns become rows. Suddenly extracting one feature at a time becomes simple. Lesson: half of data science is just getting the data into a shape you can actually work with. #Python #NumPy #DataCleaning #DataScience

45 Days ML Journey — Day 12: Support Vector Machine (SVM) Day 12 of my Machine Learning journey — diving into Support Vector Machine (SVM), a powerful algorithm used for both classification and regression tasks. Tools Used: Scikit-learn, NumPy, Pandas What is SVM? SVM is a supervised learning algorithm that finds the optimal hyperplane to separate data points of different classes with the maximum margin. Key concepts: Hyperplane : Decision boundary that separates classes Margin : Distance between the hyperplane and closest data points Support Vectors : Critical data points that define the boundary What if data is not linearly separable? SVM uses the Kernel Trick to transform data into higher dimensions where it becomes separable. Common kernels: Linear Kernel Polynomial Kernel RBF (Radial Basis Function) Kernel Why use SVM? Effective in high-dimensional spaces Works well with clear margin of separation Versatile with different kernel functions Code notebook: https://lnkd.in/gi_4TqUb Key takeaway: SVM is a robust algorithm that focuses on maximizing the margin, making it highly effective for complex classification problems. #MachineLearning #DataScience #SVM #Python #ScikitLearn #LearningInPublic #MLJourney

Revisiting Multiple Linear Regression – My ML Learning Journey As part of my ongoing machine learning journey, I revisited Multiple Linear Regression using a car dataset to strengthen my fundamentals and deepen my understanding. 🔍 What I focused on this time: • Practicing exploratory data analysis and understanding feature relationships • Visualizing how variables like HP, VOL, SP, and WT impact MPG • Building multiple models with different feature combinations • Evaluating performance using RMSE and R² score 📊 What I observed: As I added more relevant features, the model performance improved — giving a clearer picture of how multiple factors influence fuel efficiency. 💡 Why this revision mattered: Reworking the same concept helped me move beyond just “knowing” regression to actually understanding how feature selection impacts model performance. 🛠️ Tech Stack: Python | Pandas | NumPy | Matplotlib | Scikit-learn Still learning, still improving — one concept at a time. #MachineLearning #DataScience #Python #Regression #LearningJourney #DataAnalytics

🚀 Machine Learning Exercise: Improving Model Performance For this exercise, I evaluated a classification model using a Random Forest approach, focusing on precision, recall, and F1 score rather than just accuracy. While accuracy gives an overall measure of correctness, it doesn’t always reflect the types of errors within the dataset. Before modeling, tools like pivot tables can be useful for exploring patterns in the data. I then reviewed feature importance and selected the most influential variables to build a refined model using a reduced feature set (cols3). 📊 Results: Accuracy: 86.22% Precision: 85.09% Recall: 78.29% F1 Score: 81.55% This project reinforced the importance of feature selection and evaluating multiple performance metrics when building a model. #MachineLearning #DataAnalytics #Python #DataScience #FeatureEngineering #PredictiveModeling #LearningJourney

🚀 Turning Learning into Building! After learning Supervised Machine Learning, I have built a real-world project — a Heart Disease Prediction System. 🔍 This project predicts the risk of heart disease based on user input and provides real-time results through an interactive web app. 🛠️ Tech Stack: • Python • Scikit-learn • Pandas • Streamlit ✨ What I learned: • Data preprocessing & feature engineering • Model building (KNN) • Working with real-world datasets • Deploying ML models using Streamlit This project marks an important step in my journey as I move forward into more advanced Machine Learning concepts. 🚀 Live Demo: https://lnkd.in/dyVvWQrr Feel free to explore and share your feedback! github : https://lnkd.in/dRxQBYmZ #MachineLearning #Python #DataScience #Streamlit #LearningJourney #BuildInPublic

Most beginner datasets are either too simple… or too unrealistic. So I decided to create one. I built a dataset around student life — study habits, sleep patterns, social media usage, and exam performance. 👉 https://lnkd.in/ejXkATfr The idea was simple: make something beginner-friendly, but still useful for real analysis. You can: • Predict exam scores • Explore behavior patterns • Build and test ML models No complex setup. Just clean, usable data. I’ve also added a small challenge — build something with it and share your results. Curious to see what you create. #DataScience #MachineLearning #Kaggle #Python #DataProjects #LearningJourney #BeginnerFriendly