I’ve just wrapped up the first week of the <a href="https://www.linkedin.com/company/datatalks-club/" target="_blank" rel="noopener nofollow">DataTalksClub</a> by <a href="https://www.linkedin.com/in/ACoAAAW0LKEBXPrPQ9kGjzloHkzNwHOFxLiHcBU" target="_blank" rel="noopener nofollow"></a><a href="https://www.linkedin.com/in/agrigorev/" target="_blank" rel="noopener nofollow">Alexey Grigorev</a>!
🚗 Car Price Prediction using Machine Learning!
Key steps:
<ol>
<li>Data Prep &amp; EDA: Clean and explore the data.</li>
<li>Linear Regression: Build a basic predictive model.</li>
<li>Validation &amp; RMSE: Use root mean squared error to assess accuracy.</li>
<li>Feature Engineering: Improve predictions by enhancing features.</li>
<li>Categorical Variables &amp; Regularization: Handle non-numeric data and prevent overfitting.</li>
<li>Model Tuning: Optimize for better performance.</li>
<li>Final Model: Predict car prices accurately!</li>
</ol>
A full workflow from data to predictions, with room to explore more advanced techniques. 🚀 #MachineLearning #Regression #DataScience #CarPricePrediction
On to week 3! 💪

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LearningInPublic

Finished the first week of DataTalksClub's Machine Learning Zoomcamp. Key steps included data preparation, exploratory data analysis, building and validating a linear regression model, feature engineering, handling categorical variables, regularization, and model tuning to predict car prices.

🚀 Week 2 of Machine Learning Zoomcamp Complete! 🎉