Análisis de la Información

¿Cuál es el proceso general para analizar datos con Python?

The general process for analyzing data with Python typically involves the following steps:

1. Collect and import the data that you want to analyze. This can be done using a variety of tools and techniques, depending on the source and format of the data.

2. Clean and prepare the data for analysis. This typically involves tasks such as removing missing or duplicate values, transforming the data into a suitable format, and ensuring that the data is consistent and accurate.

3. Explore the data to gain insights and understand its characteristics. This can involve visualizing the data, calculating summary statistics, and identifying patterns and trends.

4. Apply appropriate statistical or machine learning methods to analyze the data. This can involve running statistical tests, fitting models to the data, or using algorithms to extract insights from the data.

5. Interpret the results of the analysis and communicate them to others. This can involve presenting the findings in a report or visualizations, and explaining the implications and limitations of the analysis.

Overall, the process for analyzing data with Python involves several steps, from collecting and cleaning the data, to applying appropriate analysis methods, to interpreting and communicating the results.

Analizando datos con Python

1. Importar conjuntos de datos

dataframe.describe() df.info

2. Organización de la información

df.replace(missing_values, new_values) data formatting: df.rename() data normalization (remap gh): scaling > .max(), Min-max > .min(), z-score > .mean().std() convert categorical variables to dummy variables: pd.get_dummies(df['fuel']

3. Análisis exploratorio de datos

Estadística descriptiva: df.describe(), box plot (quartile distribution), scatterplot Grouping data: groupby, pivot, heatmap Correlación: negative-positive linear relationship Correlación - Estadísticas: Pearson correlation, correlation heatmap Análisis de varianza ANOVA (analysis of variance): variation between groups means

4. Desarrollo de modelos

normalization > polynomial transform > linear regression Pipelines (normalization and polynomoal transform) > train pipeline Measures for in-sample evaluation:

5. Evaluación de modelo

Evaluación y refinamiento del modelo: in-ample data or training data, out-of-sample evaluation or test set Cross validation > score Overfitting and underfitting. Training error and test error Regresión de arista (ridge): ridge regression (train, predict, r^2) Grid search: hyperparameters to train the model (training, validation and test)