Turbine Electricity Prediction Project Part 3 | Data Science Internship |ML Project with HTML UI

Turbine Electricity Prediction Project Part 3| Data Science Internship |ML Project with HTML UI

In this video, we’ll build an End-to-End Data Science Project from scratch — perfect for beginners to advanced learners.
You’ll learn how to handle data, train machine learning models, and create a web UI using HTML and CSS to display results interactively.

🚀 Project: Turbine Electricity Prediction (Machine Learning + Data Mining + Web Integration)
💻 Tools & Concepts Covered:
1️⃣ Creating a virtual environment
2️⃣ Installing required Python libraries
3️⃣ Working with .ipynb and .py files
4️⃣ Understanding and exploring data features
5️⃣ Data Mining, Handling & Manipulation
6️⃣ Exploratory Data Analysis (EDA)
7️⃣ Data Visualization (Matplotlib, Seaborn)
8️⃣ Train-Test Split and Model Selection
9️⃣ Statistics (Q1, Q3, Mean, Median)
🔟 Model Evaluation & Accuracy Metrics
1️⃣1️⃣ Creating new test data
1️⃣2️⃣ Designing a simple HTML/CSS interface for predictions

🎓 This project is ideal for students doing Data Science or Machine Learning internships and for anyone who wants to learn how to connect ML models with a web interface.

📂 Technologies Used:
Python, Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn, HTML, CSS

📈 Tags:
Data Science Project | Machine Learning Internship | End to End ML Project | HTML Interface | Data Visualization | EDA | Python Project | Turbine Electricity Data | Data Mining | Model Evaluation | Statistics | Predictive Modeling

Keywords
data science project, data science internship, turbine electricity prediction, machine learning project, end to end data science, html ui project, python ml project, data visualization, data mining, data handling, data manipulation, EDA, train test split, model selection, model evaluation, statistics in python, beginner to advance ml project, data science portfolio project, internship project idea
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