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

[DebjyotiSaha]

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.bestestimator)

[JonnoFTW]

This looks like you're having an issue with sklearn, please ask on stackoverflow with formatted code, full traceback and the problem reduced to a MWE https://stackoverflow.com/help/minimal-reproducible-example

[Abhilash28snist]

can you please define X and y in this program. i have the datasets so please mention X and y (target )

[John-p-v1999]

Nee theerneda theernu... Ini theriyude pooram aayirikkum...

On Sat, 8 Jan, 2022, 1:01 am Abhilash28snist, @.***> wrote:

can you please define X and y in this program. i have the datasets so please mention X and y (target )

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[Abhilash28snist]

sorry, i cant understand your language. reply only if you know the answer.\