2025 Machine Learning Pipeline: Predict MPG Step by Step
🚗 Welcome to the MPG Prediction Machine Learning Project!
In this video, we build a complete ML pipeline to predict car fuel efficiency (Miles Per Gallon - MPG) using Python, scikit-learn, and advanced preprocessing techniques like KNN Imputation, Feature Engineering, Model Comparison, Model Saving, and more.
Perfect for AI learners, ML beginners, and data science project builders.
🕒 Chapters (SEO-Optimized Timestamps)
0:00 – Introduction & What You’ll Learn
0:03 – Overview of MPG Prediction Project
0:30 – Project Setup & Folder Structure Explained
0:54 – Importing Essential Python Libraries for ML
1:05 – Loading, Inspecting & Saving the MPG Dataset
1:28 – Exploring Dataset Insights (EDA for Beginners)
1:40 – Handling Missing Values Using KNN Imputer
1:50 – Selecting Features & Target Variable (X & y)
2:04 – Preprocessing Pipeline + Custom Functions Creation
2:15 – Training & Evaluating Multiple Regression Models
2:42 – Comparing Models Using R², MAE, RMSE, MSE
3:12 – Visualizing Model Performance (Charts & Plots)
3:28 – Saving the Best Performing Model with Joblib
4:12 – Loading Saved Model & Predicting New Input
4:45 – Writing a Clean GitHub README Using AI Tools
5:15 – Final Thoughts & Tips for ML Beginners
🚀 About This Project
This MPG Prediction Project uses various technical concepts:
🎯 Project Objectives
Predict MPG using regression algorithms
Handle missing data with KNN Imputer
Extract brand and car-type features
Compare ML models (Linear Regression, Random Forest, Gradient Boosting)
Create a reusable ML pipeline + saved model
🛠 Tech Stack
Python 3.x
pandas, numpy, scikit-learn, seaborn, matplotlib, joblib
📊 Dataset Used
MPG Dataset (from seaborn) including:
cylinders, displacement, horsepower, weight, acceleration, model_year, origin, name, mpg
🔧 Feature Engineering Highlights
Extracted car brand
Created car-type flags: is_sport, is_wagon, is_sedan, is_hatchback
Applied KNN imputation
Standardized numeric values
One-hot encoded categorical features
🤖 Models Used
Linear Regression
Random Forest
Gradient Boosting
💾 Model Deployment
The best model is saved as:
best_mpg_model_random_forest.pkl
✔ Includes preprocessing pipeline
✔ Ready for production-level predictions
📁 GitHub Repository (Full Code + Dataset + Model)
🔗 Project Repo:
https://github.com/MuhammadAhmadBhutt...
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Tags
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Hashtags
#mpgprediction #machinelearningproject #pythonml #datascienceprojects #regressionmodels #scikitlearn #featureengineering #mlpipeline #randomforest #codewithbhuttag