Regression

I've uploaded two copies of the notebook: the original *.ipynb file, together with its paired *.pdf representation.

1. Simple_linear_regression

• dataset: 1.01.Simple_linear_regression.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, linear regression, scatterplot

2. Simple_Linear_Regression_Exercise

• dataset: real_estate_price_size.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, linear regression, scatterplot

3. Multiple_linear_regression_and_Adjusted_R-squared_with_comments

• dataset: 1.02.Multiple_linear_regression.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, multiple linear regression

4. Multiple_Linear_Regression

• dataset: real_estate_price_size_year.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, multiple linear regression

5. Dummy_variables_with_comments

• dataset: Dummies.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, dummy variables, regression, scatter plot, drawing two regression lines on one plot

6. MLRegression+Dummies

• dataset: real_estate_price_size_year_view.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, regression, dummy variables

7. Making_predictions_dummies

• dataset: Dummies.csv
• packages: numpy, pandas, matplotlib, statsmodels, seaborn
• tags: descriptive statistics, dummy variables, regression, scatter plot, predictions

8. sklearn-LinearRegression

• dataset: real_estate_price_size.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, scatter plot, linear regression, R-squared, intercept, coefficients, prediction

9. sklearn_MLR

• dataset: Multiple_linear_regression.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, multiple regression, p-values

10. sklearn_MLR_2

• dataset: real_estate_price_size_year.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, multiple linear regression, intercept, coefficients, R-squared, Adjusted R-squared, predictions, p-values

11. sklearn_Feature_Selection

• dataset: 1.02. Multiple linear regression.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, multiple linear regression, standarizaton

12. sklearn_Predictions_StandardizedCoefficients

• dataset: 1.02. Multiple linear regression.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, multiple linear regression, standatization, regression with scaled features, predictions with the standardized coefficients (weights)

13. sklearn_Feature_Scaling_2

• dataset: real_estate_price_size_year.csv
• packages: numpy, pandas, matplotlib, seaborn, sklearn
• tags: descriptive statistics, multiple linear regression, data standarization, intercept, coefficients, R-squared, Adjusted R-squared, predictions, p-values

14. sklearn_Train_Test_Split

• packages: numpy, sklearn
• tags: data generated (integers 1:100), splitting the data

15. sklearn_Linear_Regression_cars

• dataset: cars.csv
• packages: numpy, pandas, statsmodels, matplotlib, sklearn, seaborn
• tags: descriptive statistics, missing values, probability distribution function, outliers, ols assumptions, log transformation, multicollinearity, dummy variables, linear regression, scaling the data, train test split, r-squared, intercept and coefficients, regression summary, testing, prediction

16. Admittance_regression

• dataset: Admittance.csv
• packages: numpy, pandas, statsmodels, matplotlib, seaborn, scipy
• tags: regression, LL-null, logistic regression curve

17. Logistic_Regression

• dataset: Example_bank_data.csv
• packages: pandas, statsmodels, matplotlib, seaborn, scipy
• tags: simple logistic regression, scatter plot, descriptive statistics

18. Logistic_regression1

• dataset: Bank_data.csv
• packages: pandas, statsmodels, matplotlib, seaborn, scipy
• tags: logistic regression, r-squared, Maximum Likelihood Estimation (MLE)

19. binarypredictors_regression+test

• dataset: test_dataset.csv, Binary predictors.csv
• packages: numpy, pandas, statsmodels, matplotlib, seaborn, scipy
• tags: logistic regression, scatter plot, testing the model, assesing accuracy, confusion matrix

20. bank_test+regression

• dataset: Bank_data_testing.csv, Bank_data.csv
• packages: numpy, pandas, statsmodels, matplotlib, seaborn, scipy
• tags: logistic regression, scatter plot, confusion matrix, testing the model,

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