I've uploaded two copies of the notebook: the original *.ipynb file, together with its paired *.pdf representation.
- Simple_linear_regression
- dataset: 1.01.Simple_linear_regression.csv
- packages: numpy, pandas, matplotlib, statsmodels, seaborn
- tags: descriptive statistics, linear regression, scatterplot
- Simple_Linear_Regression_Exercise
- dataset: real_estate_price_size.csv
- packages: numpy, pandas, matplotlib, statsmodels, seaborn
- tags: descriptive statistics, linear regression, scatterplot
- 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
- Multiple_Linear_Regression
- dataset: real_estate_price_size_year.csv
- packages: numpy, pandas, matplotlib, statsmodels, seaborn
- tags: descriptive statistics, multiple linear regression
- 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
- MLRegression+Dummies
- dataset: real_estate_price_size_year_view.csv
- packages: numpy, pandas, matplotlib, statsmodels, seaborn
- tags: descriptive statistics, regression, dummy variables
- Making_predictions_dummies
- dataset: Dummies.csv
- packages: numpy, pandas, matplotlib, statsmodels, seaborn
- tags: descriptive statistics, dummy variables, regression, scatter plot, predictions
- 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
- sklearn_MLR
- dataset: Multiple_linear_regression.csv
- packages: numpy, pandas, matplotlib, seaborn, sklearn
- tags: descriptive statistics, multiple regression, p-values
- 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
- sklearn_Feature_Selection
- dataset: 1.02. Multiple linear regression.csv
- packages: numpy, pandas, matplotlib, seaborn, sklearn
- tags: descriptive statistics, multiple linear regression, standarizaton
- 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)
- 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
- sklearn_Train_Test_Split
- packages: numpy, sklearn
- tags: data generated (integers 1:100), splitting the data
- 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
- Admittance_regression
- dataset: Admittance.csv
- packages: numpy, pandas, statsmodels, matplotlib, seaborn, scipy
- tags: regression, LL-null, logistic regression curve
- Logistic_Regression
- dataset: Example_bank_data.csv
- packages: pandas, statsmodels, matplotlib, seaborn, scipy
- tags: simple logistic regression, scatter plot, descriptive statistics
- Logistic_regression1
- dataset: Bank_data.csv
- packages: pandas, statsmodels, matplotlib, seaborn, scipy
- tags: logistic regression, r-squared, Maximum Likelihood Estimation (MLE)
- 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
- 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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