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

# -*- 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'])
    #dfvi.head()
    plt.figure(figsize=(15, 5))
    sns.barplot(x='Features', y='Importance', data=dfvi);
    plt.xticks(rotation=90) 
    plt.show()
    
#12.1 Features importance