Error in SVM as:- AttributeError: 'RandomizedSearchCV' object has no attribute 'best_estimator_' and 'NameError: name 'X_train_confirmed' is not defined" #273

import numpy as np
import pandas as pd
import matplotlib.colors as mcolors
import random
import math
import time
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, mean_absolute_error
import datetime
import operator
import matplotlib.pyplot as plt
plt.style.use('seaborn')

confirmed_cases= pd.read_csv("D:/Project/New Projects/COVID-19/time_series_covid-19_confirmed.csv")
deaths_reported= pd.read_csv("D:/Project/New Projects/COVID-19/time_series_covid-19_deaths.csv")
recover_reported= pd.read_csv("D:/Project/New Projects/COVID-19/time_series_covid-19_recovered.csv")

print(confirmed_cases.head())
print(deaths_reported.head())
print(recover_reported.head())

cols=confirmed_cases.keys()
print(cols)

confirmed= confirmed_cases.loc[:, cols[4]: cols[-1]]
deaths= deaths_reported.loc[:, cols[4]: cols[-1]]
recoveries= recover_reported.loc[:, cols[4]: cols[-1]]
print(confirmed)
print(deaths)
print(recoveries)

print(confirmed.head())

dates= confirmed.keys()
world_cases= []
total_deaths= []
mortality_rate= []
total_recovered= []

for i in dates:
confirmed_sum= confirmed[i].sum()
death_sum= deaths[i].sum()
recovered_sum= recoveries[i].sum()
world_cases.append(confirmed_sum)
total_deaths.append(death_sum)
mortality_rate.append(death_sum/confirmed_sum)
total_recovered.append(recovered_sum)

print(confirmed_sum)
print(death_sum)
print(recovered_sum)
print(world_cases)

days_since_1_22= np.array([i for i in range(len(dates))]).reshape(-1,1)
world_cases= np.array(world_cases).reshape(-1,1)
total_deaths= np.array(total_deaths).reshape(-1,1)
total_recovered= np.array(total_recovered).reshape(-1,1)
print(days_since_1_22)
print(world_cases)
print(total_deaths)
print(total_recovered)

day_in_future=10
future_forecast=np.array([i for i in range(len(dates)+ day_in_future)]).reshape(-1,1)
adjusted_dates= future_forecast[:-10]
print("future_forecast", future_forecast)

latest_confirmed= confirmed_cases[dates[-1]]
latest_deaths= deaths_reported[dates[-1]]
latest_recoveries= recover_reported[dates[-1]]
print(latest_confirmed)
print(latest_deaths)
print(latest_recoveries)

unique_countries= list(confirmed_cases["Country/Region"].unique())
print(unique_countries)

country_confirmed_cases= []
no_cases=[]
for i in unique_countries:
cases= latest_confirmed[confirmed_cases["Country/Region"]==i].sum()
if cases>0:
country_confirmed_cases.append(cases)
else:
no_cases.append(i)

for i in no_cases:
unique_countries.remove(i)

unique_countries= [k for k, v in sorted(zip(unique_countries, country_confirmed_cases), key= operator.itemgetter(1))]

for i in range(len(unique_countries)):
country_confirmed_cases[i]= latest_confirmed[confirmed_cases["Country/Region"]==unique_countries[i]].sum()

print("Confirmed cases by Country/Region")
for i in range(len(unique_countries)):
print(f'{unique_countries[i]}: {country_confirmed_cases[i]} cases')

unique_provinces= list(confirmed_cases["Province/State"].unique())
outliers= ["United Kingdom", "Denmark", "France"]
for i in outliers:
unique_provinces.remove(i)

province_confirmed_cases=[]
no_cases=[]
for i in unique_provinces:
cases= latest_confirmed[confirmed_cases["Province/State"]==i].sum()
if cases>0:
province_confirmed_cases.append(cases)
else:
no_cases.append(i)
for i in no_cases:
unique_provinces.remove(i)

for i in range(len(unique_provinces)):
print(f'{unique_provinces[i]}: {province_confirmed_cases[i]} cases')

nan_indices=[]
for i in range(len(unique_provinces)):
if type(unique_provinces[i])==float:
nan_indices.append(i)

unique_provinces= list(unique_provinces)
province_confirmed_cases= list(province_confirmed_cases)
for i in nan_indices:
unique_provinces.pop(i)
province_confirmed_cases(i)

plt.figure(figsize=(32,32))
plt.barh(unique_countries, country_confirmed_cases)
plt.title("No. of COVID-19 confirmed cases in countries")
plt.xlabel("No. of COVID-19 confirmed cases")
plt.show()

china_confirmed=latest_confirmed[confirmed_cases["Country/Region"]=="China"].sum()
outside_mainland_china_confirmed= np.sum(country_confirmed_cases) - china_confirmed
plt.figure(figsize=(16,9))
plt.barh("Mainland China", china_confirmed)
plt.barh("Outside MC", outside_mainland_china_confirmed)
plt.title("Number of confirmed cases")
plt.show()

print("Outside MC {} cases:".format(outside_mainland_china_confirmed))
print("Mainland China: {} cases".format(china_confirmed))
print("Total: {} cases".format(china_confirmed+outside_mainland_china_confirmed))

visual_unique_countries=[]
visual_confirmed_cases=[]
others=np.sum(country_confirmed_cases[10:])
for i in range(len(country_confirmed_cases[:-10])):
visual_unique_countries.append(unique_countries[i])
visual_confirmed_cases.append(country_confirmed_cases[i])

visual_unique_countries.append("Others")
visual_confirmed_cases.append(others)
plt.figure(figsize=(32,18))
plt.barh(visual_unique_countries, visual_confirmed_cases)
plt.title("No. of confirmed covid-19 in countries/region", size=20)
plt.show()

c=random.choices(list(mcolors.CSS4_COLORS.values()),k=len(unique_countries))
plt.figure(figsize=(20,20))
plt.title("Covid19 confirmed")
plt.pie(visual_confirmed_cases, colors=c)
plt.legend(visual_unique_countries, loc="best")
plt.show()

c=random.choices(list(mcolors.CSS4_COLORS.values()),k=len(unique_countries))
plt.figure(figsize=(20,20))
plt.title("Covid19 confirmed")
plt.pie(visual_confirmed_cases[1:], colors=c)
plt.legend(visual_unique_countries[-1:], loc="best")
plt.show()

kernel=["poly", "sigmoid", "rbf"]
c=[0.01, 0.1, 1, 10]
gamma=[0.01, 0.1, 1]
epsilon=[0.01, 0.1, 1]
shrinking=[True,False]
svm_grid={"kernel":kernel, "C":c, "gamma":gamma, "epsilon":epsilon, "shrinking":shrinking}

svm=SVR()
svm_search=RandomizedSearchCV(svm, svm_grid, scoring="neg_mean_squared_error", cv=3, return_train_score=True, n_jobs=-1, n_iter=40, verbose=1)
svm_search.fit(X_train_confirmed, y_train_confirmed)

print(svm_search.best_estimator_)