Скачать или смотреть House Price Prediction with Machine Learning in Python with Deployment

Want to predict house prices using machine learning and deploy your model with Streamlit? In this comprehensive tutorial, we guide you step-by-step to build a house price prediction system using Python. Learn to process real-world data, create a machine learning model, and deploy it as a user-friendly web application!

📌 What You’ll Learn in This Video:
• Setting up your Python environment for machine learning.
• Data cleaning and preprocessing with Pandas and Visualization with Matplotlib and Seaborn.
• Building and training a machine learning model with Scikit-learn.
• Evaluating model performance using metrics.
• Deploying the model as a web app using Streamlit.

📊 Topics Covered:
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Selection (Decision Tree, XGBoost etc.)
• Building a responsive Streamlit app

🚀 Tools and Libraries Used:
• Python
• Pandas, Matplotlib, Seaborn
• Scikit-learn
• Streamlit

💡 Who Is This Video For?
This tutorial is perfect for beginners, data science enthusiasts, and developers who want to dive into end-to-end machine learning projects and web app deployment.

🔗 Links Mentioned in the Video:
• Code Repository: https://github.com/TensorTitans01/Hou...
• Dataset: https://github.com/TensorTitans01/Hou...

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💬 Have Questions or Suggestions?
Drop a comment below, and I’ll be happy to help!

#MachineLearning #Python #HousePricePrediction #Streamlit #DataScience #TensorTitans