Скачать с ютуб Belarus Car Price Prediction | Part 1: Intro, Data Cleaning & EDA in Python в хорошем качестве

Belarus Car Price Prediction | Part 1: Intro, Data Cleaning & EDA in Python

Welcome to Part 1 of our Belarus Car Price Prediction series — a complete data science project in Python. In this video, we kick things off by exploring the dataset, performing data cleaning, and running initial exploratory data analysis (EDA). These crucial steps lay the groundwork for building accurate machine learning models in the next part of the series. Whether you're a beginner or intermediate data scientist, you'll gain hands-on experience with real-world datasets and see how a predictive modeling project unfolds from scratch. 📌 What you’ll learn in this video: 🔧 Project overview and prediction goals 📊 Dataset breakdown (columns, data types, unique values) 🧹 Data cleaning: Handling missing values, fixing outliers, correcting data formats ⚙️ Data preprocessing: Encoding categorical variables and feature scaling 📈 Initial EDA: Price distribution, popular car brands, year trends, and more ➡️ Don’t miss Part 2, where we dive deeper into advanced EDA and begin training machine learning models to predict used car prices in Belarus. 📂 Tools & Libraries Used: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn 🔗 Source code: https://github.com/Anshu-Gondi/Data-S... #AG_Youtube #CarPricePrediction #BelarusCars #DataScienceProject #PythonForDataScience #MachineLearning #PythonProjects #EDAinPython #UsedCars #BelarusML #DataCleaning #Preprocessing #ScikitLearn #RealWorldDataScience #CarDataset #MLWithPython #PredictiveAnalytics