Weather Forecasting with Python: Machine Learning for Beginners
Want to predict the weather using Python? This beginner-friendly tutorial will show you step-by-step how to build a weather forecasting model using machine learning in Jupyter Notebook.
What You’ll Learn:
✅ How to load and clean weather datasets
✅ Feature selection for accurate predictions
✅ Encoding categorical data for machine learning
✅ Training a weather prediction model with Python
✅ Visualizing forecast results with Plotly
Get the FREE code here: (All I ask is to please subscribe) : https://github.com/data-geek-lab/weat...
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Tools & Libraries Used:
Python | Pandas | Matplotlib | Scikit-Learn | Jupyter Notebook
This tutorial is perfect for beginners who want to dive into data science and machine learning for weather forecasting. We break down the entire process in easy-to-understand steps so you can follow along even if you're new to Python!
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URL of the dataset on Zenodo
https://zenodo.org/record/4770937/fil...
How to download Anaconda to use Jupyter Notebook for Python coding: • How to Download Anaconda for Jupyter Noteb...
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Support my channel
🔔 Don’t forget to LIKE & SUBSCRIBE for more Python & Data Analysis tutorials!
☕ Want to Buy Me A Coffee: https://buymeacoffee.com/datageekismy...
💎 Donate on PayPal : https://www.paypal.com/donate/?hosted...
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Continue your learning :
If you’re learning Python for data analytics and want a structured guide, I put everything into a book with real examples.
📘 Python for Data Cleaning → 👉 https://a.co/d/iiMzQQH
Thank you for watching.
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⏳ *Timestamps*⏳
00:00 Welcome-Intro
00:41 Why Learn Weather Prediction
01:01 Tools we will be using
01:25 The dataset we will be using
02:12 How Weather Predictions Work
02:40 Starting in Anacanda Software
03:26 Starting a New Workbook in Jupyter and renaming the title
04:29 Asking the "Question" for prediction
05:21 Step 1: Install Required Libaries
07:17 Step 2: Load the Weather Dataset using a URL (link is shown above in this description).
14:39 Step 3: Check Missing Values
15:52 Step 4: Data Processing
20:30 Step 5: Split the Data
23:06 Step 6: Build and train the Model (Sklearn-Linear Regression)
25:17 Step 7: Evaluate the Model
30:52 Step 8: Visualize the Results (maplotlib) Scatter Plot
35:12 Closing Statement (Thank you)
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