Learn practical skills, build real-world projects, and advance your career
Created 3 years ago
import warnings
warnings.filterwarnings('ignore')import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.metrics import r2_score
from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression
Biker = pd.read_csv('day.csv')
Biker.head(20)Biker.shape(730, 16)Biker.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 730 entries, 0 to 729
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 instant 730 non-null int64
1 dteday 730 non-null object
2 season 730 non-null int64
3 yr 730 non-null int64
4 mnth 730 non-null int64
5 holiday 730 non-null int64
6 weekday 730 non-null int64
7 workingday 730 non-null int64
8 weathersit 730 non-null int64
9 temp 730 non-null float64
10 atemp 730 non-null float64
11 hum 730 non-null float64
12 windspeed 730 non-null float64
13 casual 730 non-null int64
14 registered 730 non-null int64
15 cnt 730 non-null int64
dtypes: float64(4), int64(11), object(1)
memory usage: 91.4+ KB