Analisis_Exploratorio

Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Pasos 🚀

1. Se importan librerias
2. Se seleccionan los datos
3. Se convierten datos CSV
4. Se categorizan los dos dataframe
5. Se unifican los dos dataframe
6. Verificar los datos
7. Descripcion Total dataframe
8. graficas
9. Se identifica correlación
10. Asimetría
11. KNeighborsClassifier-accuracy_score.

Importar librerías 🔧

import pandas as pd 
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import skew

Analisis de regresion from sklearn.linear_model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier 
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split,cross_validate
from sklearn.preprocessing import MinMaxScaler,StandardScaler,LabelEncoder
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score

1. Leer todos los archivos 📋

url_wine_red='https://raw.githubusercontent.com/terranigmark/curso-analisis-exploratorio-datos-platzi/main/winequality-red.csv'
url_wine_white='https://raw.githubusercontent.com/terranigmark/curso-analisis-exploratorio-datos-platzi/main/winequality-white.csv'

2. Convertir arvhivos ⌨️

red=pd.read_csv(url_wine_red,delimiter=";")

3. Se categorizan los dos dataframe ⚙️

red['category']='red'
white['category']='white'

4. Se unifican los dos dataframe 📖

total_wine=red.append(white, ignore_index=True)

5. Verificar 🖇️

total_wine.dtypes

6. Descripción 7. Gráficas 📦

total_wine.describe()
total_wine.plot()
total_wine['density'].plot()
sns.set (rc={'figure.figsize': (14, 8)})
sns.countplot (total_wine['quality'])

8. Correlación 🔩

 sns.heatmap(total_wine.corr(), annot=True, fmt='.2f', linewidths=2)

9. Asimetría 🔩

skew(total_wine['alcohol'])

10. KNeighborsClassifier-accuracy_score 🔩

model_names=['KNearestNeighbors']
acc=[]
eval_acc={}
classification_model=KNeighborsClassifier()
classification_model.fit(x_train,y_train)
pred=classification_model.predict(x_test)
acc.append(accuracy_score(pred,y_test))
eval_acc={'Modelling Algorithm':model_names,'Accuracy':acc}
eval_acc

Autor ✒️

⭐️ fradurgo19