#monthlyrates

[truetravelplanner]

Discover affordable motels in San Diego with convenient weekly and monthly rates under $200. Find budget-friendly lodging options for extended stays, at https://www.truetravelplanner.com/cheap-motels-in-san-diego-2/

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[mydecorative]

Rental Property Management Tips You Need In 2023

So nowadays, you need to know that the rental market is back in action. And the business of rental property management is returning to its previous situation. So this simply means that handling your property needs more than one skill. Read on to know more.

#property #property management #rentalproperty #propertymanagementtips #tenant #monthlyrent #moving #tenant security

[yojinvestment]

To ensure a hassle-free and legally sound rental experience, we offer professional renting services to assist you in creating a rental lease agreement. We will walk you through the complexities of lease terms, monthly rent details, security deposits, and property laws. Learn more about the significance of a lease agreement while renting a home by visiting our blog at https://yojinvest.com/importance-of-lease-agreement-when-renting-a-property/.

#LeaseAgreement #RentalServices #PropertyLeasing #LegalGuidance #RentingTips #LeaseTerms #MonthlyRent #SecurityDeposits #HassleFreeRenting #PropertyRules #ExpertServices #TenantRights #LandlordTips #RentalExperience #LeasingProcess #RealEstateGuidance #YojInvest #YojRental #RentalCompany #RealEstateServices

[1stchoicebhl]

Here's one method of determining how much you should be on monthly rent

www.1stChoiceBHL.com

#Budgeting #MonthlyRent #EnoughMoneyAtTheEndOfTheMonth

[therealestatedoctor]

What do you think about this? Is this a Yay or Nay for you? What do you think the Pros & Cons will be? Let’s hear your opinion 😊 #monthlyrent #realestate #realestatelagos #lagosrealestate #realestateconsulting #realestatebroker #sanwoolu #𝕋h𝕖N𝕠1ℝe𝕒l𝔼s𝕥a𝕥e𝔻o𝕔t𝕠r (at Lafiaji Beach) https://www.instagram.com/p/CF78svMF3qs/?igshid=1lsui7j12mwam

#monthlyrent #realestate #realestatelagos #lagosrealestate #realestateconsulting #realestatebroker #sanwoolu #𝕋h𝕖n𝕠1ℝe𝕒l𝔼s𝕥a𝕥e𝔻o𝕔t𝕠r

[wowwedding-blog]

If you haven’t already hired a professional photographer to shoot your property, you are really missing out on something GREAT.

Do You Know?

1. Buyers spend 60% of their time looking at listing photos 2. Homes with high-quality photos receive 47% higher asking price 3. Professionally photographed properties can sell for up to $19,000 more 6. Listings with professional photographs sell 32% faster.

If you are keen to know more about it fix One-to-One with an expert consultant of WOW Shoots.

#photoshoot #realestatephotos #propertyphotos #UAEproperty #dubaiproperty #property #investment #buy #sell #commercial #preleased #returns #monthlyrent #rentalproperty #commercialpropertyforlease #propertyforsale #investmentproperty #realestateagent #realestate #commercialrealestate #commercialpropretyforsale #bestpropertyagents #realestategurugram #listings #realestatenews #propertysales #realestateinvestor #realestate

[theprelease-blog]

https://www.theprelease.com/property/himalaya-optics/

#prerented property investment buy sell commercial preleased returns monthlyrent rentalproperty commercialpropertyforlease propertyforsale in

[speculumfight]

Cool Filipino Punk 'zine & 7" comp with Lapu-Lapu killing Magellan on the cover #BadOmen #MonthlyRed #TigerPussy #Vinyl #PinoyPunk #Filipino #Punk #LapuLapu #FilipinoPride #BattleOfMactan (at South of Heaven)

#lapulapu #tigerpussy #battleofmactan #pinoypunk #punk #badomen #filipino #monthlyred #filipinopride #vinyl

[randomdatablogger23]

Lasso Regression Analysis

import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from sklearn.linear_model import LassoLarsCV from sklearn import preprocessing

emp_data = pd.read_csv('IBM HR Attrition Data.csv')

emp_data['Age'].describe()

emp_data.isna().sum()

emp_data.dtypes

df = emp_data[['Age','Education','Gender','OverTime','Attrition',                       'HourlyRate','YearsInCurrentRole','DailyRate',               'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction',               'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating',               'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] df['Attri'] = np.where(df['Attrition'].str.contains("Y"),1,0) df['Gender_cat'] = np.where(df['Gender'].str.contains("Female"),1,0) df['OT_cat'] = np.where(df['OverTime'].str.contains("Y"),1,0)

predict = df[['Education','Attri','MonthlyIncome','Gender_cat','OT_cat',                       'HourlyRate','YearsInCurrentRole','DailyRate',               'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction',               'MonthlyRate','PerformanceRating','NumCompaniesWorked',               'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] #'HourlyRate']]#,'Age','HourlyRate' predictors = predict.copy() predictors['Education'] = preprocessing.scale(predictors['Education']).astype('float64') predictors['Attri'] = preprocessing.scale(predictors['Attri']).astype('float64') predictors['MonthlyIncome'] = preprocessing.scale(predictors['MonthlyIncome']).astype('float64') predictors['Gender_cat'] = preprocessing.scale(predictors['Gender_cat']).astype('float64') predictors['OT_cat'] = preprocessing.scale(predictors['OT_cat']).astype('float64') predictors['HourlyRate'] = preprocessing.scale(predictors['HourlyRate']).astype('float64') predictors['YearsInCurrentRole'] = preprocessing.scale(predictors['YearsInCurrentRole']).astype('float64') predictors['DailyRate'] = preprocessing.scale(predictors['DailyRate']).astype('float64') predictors['DistanceFromHome'] = preprocessing.scale(predictors['DistanceFromHome']).astype('float64') predictors['EmployeeCount'] = preprocessing.scale(predictors['EmployeeCount']).astype('float64') predictors['JobLevel'] = preprocessing.scale(predictors['JobLevel']).astype('float64') predictors['JobSatisfaction'] = preprocessing.scale(predictors['JobSatisfaction']).astype('float64') predictors['EnvironmentSatisfaction'] = preprocessing.scale(predictors['EnvironmentSatisfaction']).astype('float64') predictors['MonthlyRate'] = preprocessing.scale(predictors['MonthlyRate']).astype('float64') predictors['PerformanceRating'] = preprocessing.scale(predictors['PerformanceRating']).astype('float64') predictors['NumCompaniesWorked'] = preprocessing.scale(predictors['NumCompaniesWorked']).astype('float64') predictors['TotalWorkingYears'] = preprocessing.scale(predictors['TotalWorkingYears']).astype('float64') predictors['TrainingTimesLastYear'] = preprocessing.scale(predictors['TrainingTimesLastYear']).astype('float64') predictors['WorkLifeBalance'] = preprocessing.scale(predictors['WorkLifeBalance']).astype('float64') predictors['YearsInCurrentRole'] = preprocessing.scale(predictors['YearsInCurrentRole']).astype('float64') predictors['TotalWorkingYears'] = preprocessing.scale(predictors['TotalWorkingYears']).astype('float64') targets = df.Age #MonthlyIncome predictors.head()

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.3) print(pred_train.shape,pred_test.shape,tar_train.shape,tar_test.shape)

model = LassoLarsCV(cv=10,precompute=False,max_iter=15).fit(pred_train,tar_train)

pred = dict(zip(predictors.columns,model.coef_)) df2 = pd.DataFrame(pred.values(),index=pred.keys(),columns=['coef']) df2['abs_coef'] = df2['coef'].apply(lambda x: abs(x)) df2.sort_values(by='abs_coef',ascending=False)

len(df2.index)

m_log_alphas = -np.log10(model.alphas_) ax = plt.gca() plt.plot(m_log_alphas, model.coef_path_.T) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',            label='alpha CV') plt.ylabel('Regression Coefficients') plt.xlabel('-log(alpha)') plt.title('Regression Coefficients Progression for Lasso Paths')

m_log_alphascv = -np.log10(model.cv_alphas_) plt.figure() plt.plot(m_log_alphascv, model.mse_path_, ':') # plot mse with dotted line plt.plot(m_log_alphascv, model.mse_path_.mean(axis=-1), 'k',         label='Average across the folds', linewidth=2) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',            label='alpha CV') plt.legend() plt.xlabel('-log(alpha)') plt.ylabel('Mean squared error') plt.title('Mean squared error on each fold')

train_error = mean_squared_error(tar_train, model.predict(pred_train)) test_error = mean_squared_error(tar_test, model.predict(pred_test)) print (f'training data MSE: {train_error}') print (f'test data MSE: {test_error}')

# R-square from training and test data rsquared_train = model.score(pred_train,tar_train) rsquared_test = model.score(pred_test,tar_test) print (f'training data R-square: {rsquared_train}') print (f'test data R-square: {rsquared_test}')

[truetravelplanner]

You can find unbeatable motel deals under $800 near you. Stay comfortable without sacrificing quality on a budget. A month-long journey? Find the perfect home away from home. Here, https://www.truetravelplanner.com/800-a-month-motel/

#monthlymotels #motelsformonth #800amonthmotel #monthlyrates #cheapmotels

[valeriaanastasia-blog]

Why is it that the bigger the pain these cramps give me the better my stomach looks? Nothing about being a girl makes any sense. And nothing pisses me off as much as my own nature. Why can't I just be a hippie about it? 🔪🔪🔪🔪🔪#monthlyrant #excusezfuckingmoi Disclosure: I might delete this post later. ✌🏼

#monthlyrant #excusezfuckingmoi

[findandselect-blog]

Student loan tips from across the ditch has been published on Find and Select Business Reviews

New Post has been published on http://www.findandselect.com/tips/student-loan-tips-from-across-the-ditch.html

Student loan tips from across the ditch

It was definitely out of sight, out of mindpaying off my student loan.

I went travelling, didn't think about it, it popped in my heada few times – but I was like I'll worry about it when I get home.

When I first left New Zealand and took therepayment holiday, I didn't know about the free online transfer payments.

Being able to go on the myIR site and they'vegot the current balance of your loan, and how many repayments you've made – and seehow far I've got left to go.

Definitely get a myIR account, because thatway you can keep your details up to date and keep informed about how much you owe.

It was really good dealing with Inland Revenuewhen I called them.

They were really friendly, so helpful.

They didn't make me feel guiltyfor not paying any of my student loans.

I find it really useful that they're ableto contact me and I'm able to contact them so easily, because when I have a problem,especially when it's about money, it can be quite stressful.

And, I found that every time I'vehad an issue they've been able to deal with it really smoothly.

My advice in hindsight, would definitely beto pay off your student loan if you can in regular installments.

Just a little bit monthlyreally helps towards those end of year compulsory repayments.

When I've paid off my student loan, I'm goingto start saving for a house or another big trip.

It's going to feel really good.

Might go on a few extra surf trips, go upto Indonesia, enjoy myself a bit more you know.

I think there'll definitely be a celebrationonce I pay off my student loan.

And then I'll look forward to seeing that money go intomy savings.

Source: Youtube

#across #ditch #from #Loan #Student #the #Tips

[1stchoicebhl]

www.1stChoiceBHL.com

#rent prices #1stChoiceBHL #FredericksburgRealEstate #FeaturedHome #MonthlyRent

[theprelease-blog]

https://www.theprelease.com/property/syndicate-bank-2/

#prerented property investment buy sell commercial preleased returns monthlyrent rentalproperty commercialpropertyforlease propertyforsale in

[randomdatablogger23]

Random Forest Attrition Analysis

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import cross_validate from sklearn.model_selection import train_test_split #train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics from sklearn import tree from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier from sklearn.ensemble import RandomForestClassifier

emp_data = pd.read_csv('IBM HR Attrition Data.csv')

emp_data.isna().sum()

emp_data.dtypes

emp_data['PerformanceRating'].describe()

df = emp_data[['Age','Education','Gender','OverTime',                       'HourlyRate','YearsInCurrentRole','DailyRate',               'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction',               'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating',               'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] df['Gender_cat'] = np.where(df['Gender'].str.contains("Female"),1,0) df['OT_cat'] = np.where(df['OverTime'].str.contains("Y"),1,0)

predictors = df[['Age','Education','Gender_cat','OT_cat',                       'HourlyRate','YearsInCurrentRole','DailyRate',               'DistanceFromHome','EmployeeCount','JobLevel','JobSatisfaction','EnvironmentSatisfaction',               'MonthlyRate','MonthlyIncome','NumCompaniesWorked','PerformanceRating',               'TotalWorkingYears','TrainingTimesLastYear','WorkLifeBalance','YearsInCurrentRole','TotalWorkingYears']] #'HourlyRate']]#,'Age','HourlyRate' targets = emp_data.Attrition

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4) print(pred_train.shape,pred_test.shape,tar_train.shape,tar_test.shape)

classifier = RandomForestClassifier(n_estimators=25) classifier = classifier.fit(pred_train,tar_train)

predictions = classifier.predict(pred_test) print(sklearn.metrics.confusion_matrix(tar_test,predictions)) sklearn.metrics.accuracy_score(tar_test,predictions)

model = ExtraTreesClassifier() model.fit(pred_train,tar_train) print(model.feature_importances_)

col_rank = {} for col,rk in zip(predictors.columns,model.feature_importances_):    col_rank[col] = rk

pred = pd.DataFrame(col_rank.values(),index=col_rank.keys(),columns={'rank'}) pred.head() pred.sort_values(by=['rank'],ascending=False)

trees=range(25) accuracy=np.zeros(25)

for idx in range(len(trees)):    classifier = RandomForestClassifier(n_estimators=idx + 1)    classifier = classifier.fit(pred_train,tar_train)    predictions = classifier.predict(pred_test)    accuracy[idx] = sklearn.metrics.accuracy_score(tar_test, predictions)

plt.cla() plt.plot(trees, accuracy);

[theprelease-blog]

prelease

www.theprelease.com

Buy Pre-rented Property And Get HIGH Returns😎 investment on Pre-leased is now easy with #Theprelease 👉https://www.theprelease.com #prerented #property #investment #buy #sell #commercial #preleased #returns #monthlyrent #rentalproperty #commercialpropertyforlease #propertyforsale #investmentproperty #realestateagent #realestate #commercialrealestate #commercialpropretyforsale #bestpropertyagents #realestategurugram #listings #realestatenews #propertysales #realestateinvestor #realestate