Machine Learning For Beginners: Car Price Prediction

Build a real-world ML project in Python using pandas, sklearn & linear regression — no experience needed!

What you'll learn

Understand how to use Linear Regression for predictive modeling in Python.

Clean and preprocess data using Pandas and Scikit-learn.

Train, evaluate, and improve a machine learning model using real-world car data.

Export and test your trained ML model using Pickle and new data.

Build an end-to-end machine learning project using Python. Explore data with visualizations using Matplotlib and Seaborn.

Requirements

No prior machine learning experience needed! Basic knowledge of Python is helpful (like variables, loops, and functions). A computer with internet access and Jupyter Notebook installed (or Google Colab). Curiosity and willingness to learn data science step by step!

Description

Are you curious about how machine learning works — but don't know where to start?This beginner-friendly course is your perfect starting point. In this hands-on project, you'll build a real-world machine learning model to predict car prices using Python and Linear Regression.Even if you're new to coding, data science, or machine learning, don’t worry! Every concept is explained in a simple and practical way, step by step.What You'll Learn:Load and explore real-world data using pandasPerform exploratory data analysis (EDA) using Matplotlib and SeabornClean and pre-process data for machine learningApply feature engineering and remove irrelevant columnsTrain a Linear Regression model using scikit-learnEvaluate model performance using MAE, MSE, and R² scoreSave the model and make predictions on new car dataUnderstand the full machine learning workflow from start to finishTools Used:Pythonpandasmatplotlib & seabornscikit-learn (sklearn)joblibBy the end of this course, you’ll have built your own car price prediction model — a strong portfolio project to showcase in interviews, apply for internships, or boost your confidence as a future data scientist.Explore the of machine learning!No prior experience needed. Just bring your curiosity — and let's build your first ML project together!

Overview

Section 1: Getting Started with the Project

Lecture 1 Course Introduction & What You’ll Build

Lecture 2 Understanding the Dataset

Lecture 3 Loading and Inspecting the Data

Section 2: Exploratory Data Analysis (EDA) – Making Sense of Data

Lecture 4 Exploring Categorical Features

Lecture 5 Data Visualization with Matplotlib

Lecture 6 Data Visualization with Seaborn

Section 3: Data Preprocessing & Feature Engineering

Lecture 7 Encoding Categorical Variables

Lecture 8 Dropping Unnecessary Features

Lecture 9 Correlation Analysis and Feature Selection

Section 4: Model Building with Linear Regression

Lecture 10 Splitting and Scaling the Data

Lecture 11 Training the Regression Model

Lecture 12 Evaluating Model Performance

Section 5: Saving the Model and Making Predictions

Lecture 13 Saving the Model with Joblib

Lecture 14 Testing the Model on New Data

Beginners who want to start their machine learning journey with a real project. Python learners looking to apply their skills in data science. Students and hobbyists interested in predictive modeling. Anyone curious about car prices, data analysis, or building ML models from scratch. Freshers preparing for interviews or building ML portfolios.