Fitting Logarithmic Curves in Python: Easy Data Modeling Tutorial: Google Colab | With Source code
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Welcome to our microtutorial on fitting logarithmic curves using Python! In this video, we will guide you through the process of modeling non-linear data sets with logarithmic independent variables. Whether you are a beginner or looking to refine your data analysis skills, this tutorial will provide you with step-by-step instructions to effectively use Python for curve fitting. We will cover:
An introduction to logarithmic curves and their applications in data modeling.
How to prepare your data for analysis.
Utilizing Python libraries like NumPy and Matplotlib for curve fitting.
Practical examples and tips to enhance your data modeling techniques.
By the end of this tutorial, you will have a solid understanding of how to fit logarithmic curves to your data and how to visualize the results. Don't forget to like, comment, and subscribe for more tutorials on data science and Python programming!
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Timecodes:
0:00 - Intro
0:28 - Why linear fitting is not suitable
1:17 - Challenge/Problem Statement
1:36 - How to import python libraries in Google colab
1:48 - How to initialise arrays in Python
2:00 - How to take natural log of an array in Python
2:10 - How to create a matrix using vectors/arrays in Python (Vertical Stacking)
2:23 - How to compute coefficients of linear regression in Python
3:00 - How to use linear regression to predict dependent variable
3:37 - How to plot linear regression line along with given data points
4:11 - How to show labels and legend on plot in Python
4:28 - Analyzing the fitted curve
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