Let us worry about your assignment instead!

We Helped With This Python Programming Assignment: Have A Similar One?

More InfoPython Coding Help

Assignment Code

# -*- coding: utf-8 -*-
Spyder Editor

This is a temporary script file.

#Dataset day.xlsx description
# instant: record index
# dteday : date
# season : season (1:springer, 2:summer, 3:fall, 4:winter)
# yr : year (0: 2011, 1:2012)
# mnth : month ( 1 to 12)
# hr : hour (0 to 23)
# holiday : weather day is holiday or not (extracted from [Web Link])
# weekday : day of the week
# workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
# weathersit : 
#  1: Clear, Few clouds, Partly cloudy, Partly cloudy
#  2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
#  3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
#  4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
# temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)
# atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)
# hum: Normalized humidity. The values are divided to 100 (max)
# windspeed: Normalized wind speed. The values are divided to 67 (max)
# casual: count of casual users
# registered: count of registered users
# cnt: count of total rental bikes including both casual and registered

#Our goal is build a model to forecast cnt
#the TARGET is cnt
#the poential predictors/features are: season, mnth, holiday, weathersit, atemp, hum, windspeed, casual,registered
#where the categorical predictors/feaures are: season, mnth, holiday, weathersit
#wher the continous predictors are: atemp, hum, windspeed, casual,registered

#step1: load the packages needed for modeling 
#hint: you need to load RandomForestRegressor insteal of RandomForestClassifier since we are modeling continous target cnt

#from sklearn.ensemble import RandomForestRegressor

#step 2 load the dataset 
#2.1. load day.xlsx file into memory using pandas

#2.2 printout the variable names/column names

#2.3 print out the size of the data
#2.4 summarize the data set using info and describle functions
#2.5 check missing values

#3 Seperate the predictors/featues by using categorical variables groups and continous variables groups.
#note that 
#the categorical predictors/feaures are: season, mnth, holiday, weathersit
#the continous predictors are: atemp, hum, windspeed, casual,registered

categ =  []
conti = []

# 4 using the loops graph the categorical and continous variables 
#5: using loops countplot all the categrical variables except holiday and set hue= 'holiday'

#6: swarmplot x = atemp, y = cnt, hue = season
#7: boxplot x = season, y = cnt
# 8: correlations with the new features
# you need to drop instant,	dteday
#plot the heatmap of the correlation
#9: set the Target to be cnt
#   set the features/predictors to be  'season', 'mnth', 'holiday', 'weathersit',
#           'atemp', 'hum', 'windspeed', 'casual','registered']
Target = 
Features = 

#10  Create training and test sets by seting test_size to be 20% and random_state =100
#11  Create a random Forest regressor model instance  and set n_estimators = 1000
#11.1  Fit to the training data

#11.2  Predict on the test data: y_pred
#11.3 print out  Score / Metrics

#12 Rank the importance of the Features by using the follwing given function
def FeaturesImportance(data,model):
    features = data.columns.tolist()
    fi = model.feature_importances_
    sorted_features = {}
    for feature, imp in zip(features, fi):
        sorted_features[feature] = round(imp,3)

    # sort the dictionnary by value
    sorted_features = OrderedDict(sorted(sorted_features.items(),reverse=True, key=lambda t: t[1]))

    #for feature, imp in sorted_features.items():
        #print(feature+" : ",imp)

    dfvi = pd.DataFrame(list(sorted_features.items()), columns=['Features', 'Importance'])
    plt.figure(figsize=(15, 5))
    sns.barplot(x='Features', y='Importance', data=dfvi);
#12.1 Features importance

Frequently Asked Questions

Is it free to get my assignment evaluated?

Yes. No hidden fees. You pay for the solution only, and all the explanations about how to run it are included in the price. It takes up to 24 hours to get a quote from an expert. In some cases, we can help you faster if an expert is available, but you should always order in advance to avoid the risks. You can place a new order here.

How much does it cost?

The cost depends on many factors: how far away the deadline is, how hard/big the task is, if it is code only or a report, etc. We try to give rough estimates here, but it is just for orientation (in USD):

Regular homework$20 - $150
Advanced homework$100 - $300
Group project or a report$200 - $500
Mid-term or final project$200 - $800
Live exam help$100 - $300
Full thesis$1000 - $3000

How do I pay?

Credit card or PayPal. You don't need to create/have a Payal account in order to pay by a credit card. Paypal offers you "buyer's protection" in case of any issues.

Why do I need to pay in advance?

We have no way to request money after we send you the solution. PayPal works as a middleman, which protects you in case of any disputes, so you should feel safe paying using PayPal.

Do you do essays?

No, unless it is a data analysis essay or report. This is because essays are very personal and it is easy to see when they are written by another person. This is not the case with math and programming.

Why there are no discounts?

It is because we don't want to lie - in such services no discount can be set in advance because we set the price knowing that there is a discount. For example, if we wanted to ask for $100, we could tell that the price is $200 and because you are special, we can do a 50% discount. It is the way all scam websites operate. We set honest prices instead, so there is no need for fake discounts.

Do you do live tutoring?

No, it is simply not how we operate. How often do you meet a great programmer who is also a great speaker? Rarely. It is why we encourage our experts to write down explanations instead of having a live call. It is often enough to get you started - analyzing and running the solutions is a big part of learning.

What happens if I am not satisfied with the solution?

Another expert will review the task, and if your claim is reasonable - we refund the payment and often block the freelancer from our platform. Because we are so harsh with our experts - the ones working with us are very trustworthy to deliver high-quality assignment solutions on time.

Customer Feedback

"Thanks for explanations after the assignment was already completed... Emily is such a nice tutor! "

Order #13073

Find Us On

soc fb soc insta

Paypal supported