I CREATE WEATHER PREDICTION MACHINE LEARNING MODEL USING PYTHON смотреть онлайн

I CREATE WEATHER PREDICTION MACHINE LEARNING MODEL USING PYTHON

Welcome to another exciting tutorial on machine learning with Python! In this video, we'll dive into the world of weather data analysis using Python and explore how to build a Linear Regression model to predict temperatures based on various weather parameters.

In this tutorial, we'll cover the following topics:
1. Generating Synthetic Weather Data: We'll create synthetic weather data for humidity, pressure, wind speed, and temperature to work with.

2. Data Visualization: You'll learn how to visualize the data using Matplotlib, creating scatter plots to understand relationships between weather parameters and temperature.

3. Data Preprocessing: We'll split the data into features (X) and the target variable (y), and then further divide it into training and testing sets.

4. Building a Linear Regression Model: We'll create a Linear Regression model using scikit-learn and train it with the training data.

5. Model Evaluation: We'll evaluate the model's performance using metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE) to assess its accuracy.

6. Visualizing Predictions: You'll see how well the model predicts temperatures by visualizing the actual vs. predicted values.

7. Making Predictions: We'll demonstrate how to use the trained model to make predictions on new weather data.

This tutorial is perfect for beginners looking to get hands-on experience with data analysis and machine learning in Python. By the end of this video, you'll have a solid understanding of Linear Regression and how to apply it to real-world data.

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