Python Data Science Tutorial
Python Data Processing
Python Data Visualization
Statistical Data Analysis
Selected Reading
- Python Data Science - Matplotlib
- Python Data Science - SciPy
- Python Data Science - Numpy
- Python Data Science - Pandas
- Python Data Science - Environment Setup
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Python Data Processing
- Python Stemming and Lemmatization
- Python word tokenization
- Python Processing Unstructured Data
- Python Reading HTML Pages
- Python Data Aggregation
- Python Data Wrangling
- Python Date and Time
- Python NoSQL Databases
- Python Relational databases
- Python Processing XLS Data
- Python Processing JSON Data
- Python Processing CSV Data
- Python Data cleansing
- Python Data Operations
Python Data Visualization
- Python Graph Data
- Python Geographical Data
- Python Time Series
- Python 3D Charts
- Python Bubble Charts
- Python Scatter Plots
- Python Heat Maps
- Python Box Plots
- Python Chart Styling
- Python Chart Properties
Statistical Data Analysis
- Python Linear Regression
- Python Chi-square Test
- Python Correlation
- Python P-Value
- Python Bernoulli Distribution
- Python Poisson Distribution
- Python Binomial Distribution
- Python Normal Distribution
- Python Measuring Variance
- Python Measuring Central Tendency
Selected Reading
- Who is Who
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Python Linear Regression
Python - Linear Regression
In Linear Regression these two variables are related through an equation, where exponent (power) of both these variables is 1. Mathematically a pnear relationship represents a straight pne when plotted as a graph. A non-pnear relationship where the exponent of any variable is not equal to 1 creates a curve.
The functions in Seaborn to find the pnear regression relationship is regplot. The below example shows its use.
import seaborn as sb from matplotpb import pyplot as plt df = sb.load_dataset( tips ) sb.regplot(x = "total_bill", y = "tip", data = df) plt.show()
Its output is as follows −
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