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House Price Prediction with Machine Learning in Python with Deployment
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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:
👉 Don’t Forget to Subscribe!
If you enjoy content on machine learning, Python coding, and data science projects, subscribe to Tensor Titans for more tutorials and practical projects.
🔔 Hit the Bell Icon to get notified about new videos every week!
💬 Have Questions or Suggestions?
Drop a comment below, and I’ll be happy to help!
#MachineLearning #Python #HousePricePrediction #Streamlit #DataScience #TensorTitans
📌 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:
👉 Don’t Forget to Subscribe!
If you enjoy content on machine learning, Python coding, and data science projects, subscribe to Tensor Titans for more tutorials and practical projects.
🔔 Hit the Bell Icon to get notified about new videos every week!
💬 Have Questions or Suggestions?
Drop a comment below, and I’ll be happy to help!
#MachineLearning #Python #HousePricePrediction #Streamlit #DataScience #TensorTitans
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