Regression & Time Series Analysis in Python | Linear Models & Forecasting (Python 12)
welcome to Python 12 of our data analysis series!
In this tutorial, you’ll learn how to perform both regression analysis and time series analysis in Python using popular libraries like statsmodels, scikit-learn, and pandas.
📉📈 What You’ll Learn:
🔹 Regression Analysis:
Linear regression using scikit-learn and statsmodels
Visualizing regression lines with Matplotlib
Interpreting coefficients, R-squared, and p-values
Predicting outcomes using regression models
🔹 Time Series Analysis:
Time series data structure with pandas
Plotting time series trends and seasonality
Smoothing techniques and rolling averages
Forecasting with ARIMA models using statsmodels
Time-based train-test splitting
✅ Perfect for students, data scientists, and researchers working with trends, forecasts, and predictive models in Python.
📌 Timestamps:
00:00 Introduction
00:45 Linear Regression with scikit-learn
03:00 Regression with statsmodels
04:30 Visualizing regression results
06:00 Intro to time series data with pandas
07:00 Trend analysis and moving averages
08:00 Forecasting with ARIMA
09:30 Summary & Best Practices
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