Learn how to Develop and Deploy a Simple Machine Learning Model | 2025 | Code Provided

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In this comprehensive tutorial, we guide you through the entire process of developing and deploying a machine learning regression model to predict car prices 🚘. From data preprocessing to deploying your model with Streamlit and creating a Power BI Dashboard, this video covers it all!

What you’ll learn in this video:
1. Understanding the Machine Learning Process: A step-by-step explanation of building ML models.
2. Data Preprocessing: Cleaning, handling missing values, and transforming variables.
3. Data Visualization: Exploring the data with pair plots, box plots, histograms, and bar charts. We also investigate data distributions and correlations using a Pearson correlation heatmap.
4. Feature Engineering: Converting categorical variables into numerical representations.
5. Model Development: Using a Decision Tree algorithm for feature importance and running a regression model to predict car prices.
6. Model Validation: Splitting the data using holdout validation and evaluating performance.
7. Deployment:
o Building an interactive Streamlit app to predict car prices with user inputs.
o Creating a Power BI Dashboard to visualize predictions and insights.
Tools used:
• Python (Pandas, Seaborn, Scikit-learn)
• Streamlit for app development
• Power BI for dashboard creation
By the end of this video, you’ll have a fully functional ML model, an interactive web app, and a professional dashboard to showcase your predictions! 🎯
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🔗 Chapters:
00:00 – Intro
02:05 – The ML Process
03:01 – Problem formulation
03:18 – Loading the Raw Data
04:15 – Data Preprocessing
11:57 – Data Visualizations
19:18 – Preparing the DF for Modelling
21:48 – Correlations
22:40 – Feature Importance
24:08 – Hold Out Validation
24:55 – Running Regression
25:57 – Evaluation Metrics