106 ΠΏΠΎΠ΄ΠΏΠΈΡΡΠΈΠΊΠΎΠ²
πΠΠΎΠ΄ΡΠ»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ³ΠΎΠ΄Ρ
β’ ΠΠΌΠΏΠΎΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊ
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import re
import missingno as mso
from scipy import stats
from scipy.stats import ttest_ind
from scipy.stats import pearsonr
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report
import pandas as pd
β’ Π§ΡΠ΅Π½ΠΈΠ΅ CSV-ΡΠ°ΠΉΠ»Π°
data=pd.read_csv("/content/seattle-weather.csv")
data.head()
β’ Π€ΠΎΡΠΌΠ° Π΄Π°Π½Π½ΡΡ
data.shape
(1461, 6)
import warnings
warnings.filterwarnings('ignore')
sns.countplot("weather",data=data,palette='hls')
ΠΠΊΠΎΠ»ΠΎ ΠΌΠΈΠ½ΡΡΡ
27Β ΠΈΡΠ»ΡΒ 2023