Student Performance Dashboard with React, FastAPI, and Machine Learning

Day 46/60 of #60DaysOfMiniProjects Built a Smart Study & Mood Tracker using Flask! Excited to share my latest project where I combined productivity tracking with a touch of intelligent suggestions Features: • Track daily study sessions with mood & notes • Smart suggestions based on mood and activity • Productivity score calculation • Daily streak tracking • Search, edit, and manage past sessions • Clean and simple user interface Tech Stack: Python | Flask | JSON | HTML/CSS This project helped me understand how small data insights can improve consistency and focus in daily routines. Would love your feedback and suggestions to improve it further! #Python #Flask #WebDevelopment #StudentProjects #Productivity #CodingJourney #OpenToLearn

My first ML project is live on GitHub. Built a Random Forest model trained on 1,460 real house sales that predicts sale prices with a Mean Absolute Error of ~$17,000. Used SHAP values to explain which features drive predictions — turns out overall quality and living area matter most. Tech used: Python, pandas, scikit-learn, SHAP https://lnkd.in/gC4DhQbg #DataScience #MachineLearning #Python #Portfolio

🐍 Moving beyond basic Pandas… When datasets get bigger, how you write Pandas code starts to matter a lot. Here are a few techniques I’ve been learning to make analysis faster, cleaner, and more scalable: ✔ Vectorization instead of loops ✔ Using .loc[] and .iloc[] correctly ✔ Choosing apply() vs map() wisely ✔ Writing readable pipelines with method chaining ✔ Handling missing data before analysis Small improvements → Huge impact on real-world datasets 📊 Which Pandas technique improved your workflow the most? 👇 #Python #Pandas #DataAnalytics #LearningInPublic #AspiringDataAnalyst

🚀 Excited to share my latest Machine Learning project — Forest Fire Weather Index Prediction Web App 🔥🌲 Built using Python, Flask, Scikit-learn, Pandas, and NumPy. ✅ Ridge Regression Model ✅ Real-time prediction system ✅ Flask-based web application ✅ Data preprocessing using StandardScaler This project helped me gain hands-on experience in Machine Learning model deployment and web application development. 🔗 GitHub Repository : https://lnkd.in/eJKfWuw4 I’d love to hear your feedback and suggestions! #MachineLearning #Python #Flask #DataScience #AI #WebDevelopmentv

📅 Day 17/30 — House Recommendation System (Python + ML + Streamlit) 🏡🤖 🔹 Project Overview: Built a House Recommendation System that helps users find properties based on their specific requirements using Machine Learning. Designed an interactive system with Streamlit to take user inputs and return relevant house suggestions. 🔹 Tools Used: Python | Machine Learning | Streamlit 🔹 Key Features: • Personalized house recommendations based on user preferences 🏡 • User input-driven filtering (budget, location, features) 🎯 • Real-time property suggestions ⚡ • Data preprocessing and feature engineering 🔧 • Efficient recommendation logic using similarity techniques 🤖 🔹 What I Learned: • Building practical recommendation systems using ML • Handling user input and mapping it to meaningful outputs • Feature engineering for improving recommendations • Creating interactive applications with Streamlit • Applying ML to solve real-world user problems 🔗 GitHub Repository: https://lnkd.in/dH79ives #Python #MachineLearning #Streamlit #RecommendationSystem #DataScience #MLProjects #TechProjects #30DaysOfCode 🚀

Let's Become ML Engineers Together — Phase 1: Foundation (Part-1) Python for Machine Learning There are many apps and environments where you can write and run Python code, But I would suggest Google Colab 1️⃣ Basics Variables & types— No type declarations needed. Python figures it out. Math & comparisons if / elif / else for loops 2️⃣ Data Structures List — ordered, changeable Dict — key → value pairs Tuple — immutable list Set — unique values List comprehension — compact loops 3️⃣ Functions & OOP 🌟 Defining functions Default arguments Lambda — one-line function Defining a class Inheritance 💭 You can learn from https://lnkd.in/gsYx6j8J #ML #Python #Basic

#Day26 --Built a Student Mark Predictor ML model that forecasts exam scores based on study hours using Linear Regression (y = 3.93x + 50.44). Developed with Python, Flask, and Scikit-learn. 1. 📊 Data Collection Collected data with two columns — Study Hours and Exam Marks Example: 2 hours → 58 marks, 5 hours → 70 marks 2. 🤖 Model Training (train_model.py) Used Linear Regression algorithm Fed the study hours vs marks data to the model Model learned the pattern and created the formula: ▪️ Predict exam scores from study hours using Linear Regression | Python · Flask · Scikit-learn  ▪️ Our GitHub repo URL will look like:-https://lnkd.in/g8U-CCds Thankyou.

Started this journey feeling completely lost. Python didn’t make sense, SQL looked like a foreign language, and you kept questioning if you were cut out for this. I need you to know; you figure it out. Those same Python concepts will click. SQL will start to feel natural. And you will grow into data science, even understanding how machine learning models work. It’s consistency, patience, and small wins over time. So relax, keep going, and trust the process. And if you are just starting too, it’s okay to feel lost… just don’t stop. #RisewithTechCrush #Tech4Africans #LearningwithTechCrush

I understood NumPy better when I applied it to real data 👇 Learning concepts is one thing… But using them on actual data is different. So I tried a simple example: 👉 Dataset: list of student marks Task: Add 5 bonus marks to every student Using Python list: - needed a loop - more lines of code Using NumPy: - converted list → array - added 5 in a single step That’s it. What I realized: NumPy is not just about syntax. It’s about handling data efficiently at scale. Even a small example made it clear: - less code - faster execution - cleaner logic Now I’m focusing more on applying concepts, not just learning them. If you're learning NumPy, try this: 👉 Take any small dataset and apply operations on it That’s where real understanding begins. What’s one concept you learned but haven’t applied yet? #NumPy #Python #DataScience #DataEngineering #MachineLearning #CodingJourney #TechLearning

I used to think Python was just about writing code. That changed when I started working with libraries. Once I got into NumPy, Pandas, and the rest, I realized it’s less about coding and more about solving problems with the right tools. Each library started to click in its own way: • Pandas → messy, real-world data that needs cleaning and shaping • NumPy → handling performance-heavy numerical operations • Matplotlib & Seaborn → actually understanding what the data is saying • Scikit-learn → taking it a step further with predictions But the biggest shift? Not just learning the libraries… 👉 Learning when to use which one That’s what made everything start to make sense. I’m still learning, but now I approach problems differently: Not “how do I code this?” But “what’s the right tool for this?” Curious - what’s the one Python library you use the most, and why? #Python #DataAnalytics #MachineLearning #Libraries