数据分析代 modeling代写 neural network代写 dataset代写

A data analysis of Holland housing price

数据分析代写 This is a data analysis on Holland’s housing price. This report will be mainly consisted of two parts: housing data

This is a data analysis on Holland’s housing price. This report will be mainly consisted of two parts: housing data(describing inner information and their impact on housing price) and full_data(describing outer information and their impact on housing price). For each part, we will process as follows:

dealing with missing values 数据分析代写
exploratory analysis
modeling(gradient boosting, support vector regression and neural network) and conclusion

In [1]:

# First, import some necessary librairies

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

%matplotlib inline

import matplotlib.pyplot as plt # Matlab-style plotting

In [2]: data analysis1代写

#Now let’s import and put the train and test datasets in pandas dataframe

full_data = pd.read_excel(‘full_data.xlsx’)

housing = pd.read_excel(‘housing.xls’)

In [3]:

##display the first five rows of the full_data.

full_data.head()

Out[3]:

In [4]:

#display the first five rows of the housing dataset.

housing.head(5)

In [5]:

#check the numbers of samples and features

print(“The full_data size before dropping Id feature is : {} “.format(full_data.s

print(“The housing data size before dropping Id feature is : {} “.format(housing.

The full_data size before dropping Id feature is : (381, 24)

The housing data size before dropping Id feature is : (1689, 19)

Part 1: housing data 数据分析代写

Dealing with missing values

From the data above, we obviously find out that some of columns in housing is problematic. For example, Sale price and living area are both strings instead of numerical values. Next, we fix each column one by one.

Sale Price 数据分析代写

In [6]:

# Since our target is Sale Price, nan in Sale Price column is meaningless data

# first drop all rows with nan Sale Price

housing = housing.dropna(subset = [“Sale Price”])

print(housing.shape)

housing.index = range(housing.shape[0])

(1681, 19)

Notice here we delete 8 rows which does not contain Sale Price information

Above we drop seven rows without Sale Price information each price is a stirng, we try to transform string into numerical values 数据分析代写

In [7]:

# fix sale price

def fixsaleprice(input):

ans = ”

for ii in range(len(input)):

if(input[ii].isdigit()):

ans=ans+input[ii]data analysis1代写

return int(ans)

for ii in range(housing.shape[0]):

string = housing.iloc[ii,2]

housing.iloc[ii,2] = fixsaleprice(string)

House type 数据分析代写

Some of house type is missing. I will fill all na with empty strings

In [8]:

housing[“House type”] = housing[“House type”].fillna(“None”)

In [9]:

housing[“House type”] = housing[“House type”].astype(str)

YEAR BUILD 数据分析代写

For “YEAR BUILD”, some values are missing, some are wrong(year 2019 in column “YEAR BUILD”), some are actually strings. We fix them one by one.

In [10]:

for ii in range(housing.shape[0]):

if(housing.iloc[ii,4] == “Na 2011”):

housing.iloc[ii,4] = “2011”

if(housing.iloc[ii,4] == “Voor 1906”):

housing.iloc[ii,4] = “1906”

housing.iloc[ii,4] = float(housing.iloc[ii,4])数据分析代写

In [11]:

# To fill in all nan values in “YEAR BUILD”, replace all nan in “YEAR BUILD” with

median = housing[“YEAR BUILD”].median(skipna = True)

housing[“YEAR BUILD”] = housing[“YEAR BUILD”].fillna(median)

# check whether there is still missing values in this column

print(housing[“YEAR BUILD”].isnull().values.any())

False

In [12]:

# Use 2018 – this column’s value to replace this column

for ii in range(housing.shape[0]):

housing.iloc[ii,4] = (2018 – housing.iloc[ii,4])

Living area 数据分析代写

Note each Living area is strings, I transform of them into numerical values.

In [13]:

for ii in range(housing.shape[0]):

string = housing[“Living area”].iloc[ii]

if(not pd.isnull(string)):

housing[“Living area”].iloc[ii] = int(string[:–2])

median = housing[“Living area”].median(skipna = True)

housing[“Living area”] = housing[“Living area”].fillna(median)

housing[“Living area”].isnull().values.any()数据分析代写

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stab le/indexing.html#indexing-view-versus-copy (http://pandas.pydata.org/pandas-doc s/stable/indexing.html#indexing-view-versus-copy)

self._setitem_with_indexer(indexer, value)

Out[13]: False

External storage space data analysis1代写

With similar method, I transform “External storage space” into numerical values.

In [14]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“External storage space”].iloc[ii])):

housing[“External storage space”].iloc[ii] = int(housing[“External storage

median = housing[“External storage space”].median(skipna=True)

housing[“External storage space”] = housing[“External storage space”].fillna(media

print(housing[“External storage space”].isnull().values.any())

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame 数据分析代写

self._setitem_with_indexer(indexer, value) False

Plot area

In [15]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“Plot area”].iloc[ii])):

housing[“Plot area”].iloc[ii] = fixsaleprice(housing[“Plot area”].iloc[ii

median = housing[“Plot area”].median(skipna=True)

housing[“Plot area”] = housing[“Plot area”].fillna(median)

print(housing[“Plot area”].isnull().values.any())数据分析代写

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value) False

Volume 数据分析代写

In [16]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“Volume”].iloc[ii])):

housing[“Volume”].iloc[ii] = fixsaleprice(housing[“Volume”].iloc[ii][:–2]

median = housing[“Volume”].median(skipna=True)

housing[“Volume”] = housing[“Volume”].fillna(median)

print(housing[“Volume”].isnull().values.any())

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value) False

total rooms数据分析代写

In [17]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“total rooms”].iloc[ii])):

housing[“total rooms”].iloc[ii] = int(housing[“total rooms”].iloc[ii][:–7

median = housing[“total rooms”].median(skipna=True)

housing[“total rooms”] = housing[“total rooms”].fillna(median)

print(housing[“total rooms”].isnull().values.any())

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value) False

sleeprooms

In [18]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“sleeprooms”].iloc[ii])):

housing[“sleeprooms”].iloc[ii] = int(housing[“sleeprooms”].iloc[ii][:–11]

median = housing[“sleeprooms”].median(skipna=True)

housing[“sleeprooms”] = housing[“sleeprooms”].fillna(median)

print(housing[“sleeprooms”].isnull().values.any())

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame 数据分析代写

self._setitem_with_indexer(indexer, value) False

bathrooms 数据分析代写

In [19]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“bathrooms”].iloc[ii])):

housing[“bathrooms”].iloc[ii] = fixsaleprice(housing[“bathrooms”].iloc[ii

housing[“bathrooms”] = housing[“bathrooms”].fillna(0)

housing[“bathrooms”].isnull().values.any()

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value)

Out[19]: False

separate toilet 数据分析代写

In [20]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“separate toilet”].iloc[ii])):

housing[“separate toilet”].iloc[ii] = fixsaleprice(housing[“separate toile

housing[“separate toilet”] = housing[“separate toilet”].fillna(0)

housing[“separate toilet”].isnull().values.any()

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value)

Out[20]: False

Number of floors\n

In [21]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“Number of floors\n”].iloc[ii])):

housing[“Number of floors\n”].iloc[ii] = fixsaleprice(housing[“Number of f

housing[“Number of floors\n”] = housing[“Number of floors\n”].fillna(1)

housing[“Number of floors\n”].isnull().values.any()

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:数据分析代写

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value)

Out[21]: False

EERGY LABELn

Energy label are quite hard to understand, so I throw it away.

In [22]:housing = housing.drop([“Energy label”],axis = 1)

Heating type数据分析代写

In [23]:

housing[“Heating type”] = housing[“Heating type”].fillna(“None”)

housing[“Heating type”].isnull().values.any()

Out[23]: False

Ownership situation

In [24]:

housing[“Ownership situation”] = housing[“Ownership situation”].fillna(“None”)

housing[“Ownership situation”].isnull().values.any()

Out[24]: False

Backyard 数据分析代写

In [25]:

for ii in range(housing.shape[0]):

if(not pd.isnull(housing[“Backyard”].iloc[ii])):

if(not housing[“Backyard”].iloc[ii].isdigit()):

housing[“Backyard”].iloc[ii] = 0

C:\Users\25735\Anaconda3\lib\site-packages\pandas\core\indexing.py:194: Setting WithCopyWarning:数据分析代写

A value is trying to be set on a copy of a slice from a DataFrame

self._setitem_with_indexer(indexer, value)

Out[25]: False

Barn storage

In [26]:housing[“Barn storage”] = housing[“Barn storage”].fillna(“None”)

Adres, Postcode数据分析代写

In [27]:housing = housing.drop([“Adres”,”Postcode”], axis = 1)

In [28]:

# check whether there is still missing values

housing.isnull().values.any()数据分析代写

Out[28]: False

In [29]:

# transform Sale Price into by using log transformation, because we are more conce

train_label = np.log(housing[“Sale Price”].astype(int))

In [30]:

# Use one-hot encoding to encode categorical features

train_data = pd.concat([housing[[‘YEAR BUILD’, ‘Living area’, ‘External storage s

In [31]:

# have a look at training data数据分析代写

train_data.head()

Out[31]:

Exploratory data analysis

correlation heat map between numerical values

In [32]:

import seaborn as sns

import matplotlib.pyplot as plt

sns.set(style=”white”)

train = housing[[‘Sale Price’,‘YEAR BUILD’, ‘Living area’, ‘External storage space

train[“Sale Price”] = train[“Sale Price”].astype(“int”)数据分析代写

# Compute the correlation matrix

corr = train.corr()

# Generate a mask for the upper triangle

mask = np.zeros_like(corr, dtype=np.bool)

mask[np.triu_indices_from(mask)] = True

# Set up the matplotlib figure

f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap

cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio

sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,

square=True, linewidths=.5, cbar_kws={“shrink”: .5})数据分析代写

C:\Users\25735\Anaconda3\lib\site-packages\ipykernel_launcher.py:7: SettingWith CopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

import sys

Out[32]: <matplotlib.axes._subplots.AxesSubplot at 0x23cf1e39be0>

To visualize correlation better, I choose three features to and use scatterplot to see the trend.

In [35]:

%matplotlib inline

plt.subplot(3,1,1)

plt.scatter(train_data[“Living area”],train_label,c=‘r’)

plt.title(‘Sale Price vs Living area’)数据分析代写

plt.xlabel(‘Living area’)

plt.subplot(3,1,2)

plt.scatter(train_data[“total rooms”],train_label,c=‘g’)

plt.xlabel(‘total rooms’)数据分析代写

plt.subplot(3,1,3)

plt.scatter(train_data[“Backyard”],train_label,c=‘b’)

plt.ylabel(“Backyard”)

plt.show()

Modeling

In [36]:

from sklearn.model_selection import KFold, cross_val_score, train_test_split

from sklearn.metrics import mean_squared_error

import xgboost as xgb

Define a cross validation strategy

I use the cross_val_score function of Sklearn. However this function has not a shuffle attribut, I add then one line of code, in order to shuffle the dataset prior to cross-validation数据分析代写

In [37]:

#Validation function

n_folds = 5

def rmsle_cv(model):

kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train_data.va

rmse= np.sqrt(–cross_val_score(model, train_data.values, train_label, scoring=

return(rmse)

XGBoost

In [38]:

model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,

learning_rate=0.05, max_depth=3,

min_child_weight=1.7817, n_estimators=2200, 数据分析代写

reg_alpha=0.4640, reg_lambda=0.8571,

subsample=0.5213, silent=1,

random_state =7, nthread = –1)

Let’s see how these base models perform on the data by evaluating the cross-validation rmsle error

In [39]:

score = rmsle_cv(model_xgb)

print(“Xgboost score: {:.4f} ({:.4f})\n”.format(score.mean(), score.std()))

Xgboost score: 0.2939 (0.0385)

In [40]:

model_xgb.fit(train_data,train_label)数据分析代写

Out[40]: XGBRegressor(base_score=0.5, booster=’gbtree’, colsample_bylevel=1, colsample_bytree=0.4603, gamma=0.0468, learning_rate=0.05, max_delta_step=0, max_depth=3, min_child_weight=1.7817, missing=None, n_estimators=2200, n_jobs=1, nthread=-1, objective=’reg:linear’, random_state=7, reg_alpha=0.464, reg_lambda=0.8571, scale_pos_weight=1, seed=None, silent=1, subsample=0.5213)

Now plot importance of 10 most important features

In [41]:

Out[41]: <matplotlib.axes._subplots.AxesSubplot at 0x23cf4826b38>

xgb.plot_importance(model_xgb,max_num_features = 10)

support vectore regression 数据分析代写

In [42]:

from sklearn import svm

model_svr = svm.SVR()

score = rmsle_cv(model_svr)

print(“rmse of support vector regression is {}, standard error is {}”.format(score

rmse of support vector regression is 0.5403059119987044, standard error is 0.09 083728321588952

neural network

In [44]:

from keras import Sequential

model = Sequential()

from keras.layers import Dense

model.add(Dense(units=16, activation=‘relu’, input_dim=139))

model.add(Dense(units=16, activation=‘linear’, input_dim=16))数据分析代写

model.add(Dense(units=1, activation=‘relu’, input_dim=16))

model.compile(loss=‘mean_squared_error’, optimizer=‘sgd’, metrics=[‘accuracy’])

model.fit(x=train_data,y=train_label,validation_split=0.1,epochs = 5,batch_size =

Train on 1512 samples, validate on 169 samples Epoch 1/5

1512/1512 [==============================] – 0s 209us/step – loss: nan – acc:

0.0000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 2/5

1512/1512 [==============================] – 0s 96us/step – loss: nan – acc: 0.

0000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 3/5数据分析代写

1512/1512 [==============================] – 0s 100us/step – loss: nan – acc:

0.0000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 4/5

1512/1512 [==============================] – 0s 119us/step – loss: nan – acc:

0.0000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 5/5

1512/1512 [==============================] – 0s 161us/step – loss: nan – acc:

0.0000e+00 – val_loss: nan – val_acc: 0.0000e+00

Out[44]: <keras.callbacks.History at 0x23cf9080e48>数据分析代写

With so limited data, it’s hard to train a neural network

Final conclusion

Among three models, XGBoost performs best, the most important several features are “Volume”,”YEAR BUILD”,”Living area”,”Plot area”,”External storage space”,”total rooms”.

Part 2: full data数据分析代写

Missing values

I think region is unnecessary for our analysis, so I choose to drop it.

In [45]:

full_data = full_data.drop([“Regions”],axis = 1)

For “Period”, I choose to use 2018 to minus this column

In [46]:

full_data[“Period”] = 2018 – full_data[“Period”]数据分析代写

For the rest of the data, since missing values are so limited and missing values probably means no cars or limited road length in kms, I choose to fill in 0s

In [47]:

full_data = full_data.fillna(0)数据分析代写

Exploratory data analysis

correlation heatmap

In [48]:

sns.set(style=”white”)

train = full_data

# Compute the correlation matrix

corr = train.corr()

# Generate a mask for the upper triangle

mask = np.zeros_like(corr, dtype=np.bool)数据分析代写

mask[np.triu_indices_from(mask)] = True

# Set up the matplotlib figure

f, ax = plt.subplots(figsize=(11, 9))

# Generate a custom diverging colormap

cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio

sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,

square=True, linewidths=.5, cbar_kws={“shrink”: .5})

Out[48]: <matplotlib.axes._subplots.AxesSubplot at 0x23cf9687ba8>

scatterplot with respect to highly correlated variables

In [49]:

%matplotlib inline

plt.subplot(3,1,1)

plt.scatter(full_data[“Average household income(owning a house)1 000 euro”],full_d

plt.title(‘price vs income’)

plt.xlabel(‘income’)数据分析代写

plt.subplot(3,1,2)

plt.scatter(full_data[“Period”],full_data[“Average house price *1000”],c=‘g’)

plt.xlabel(‘Period’)

plt.subplot(3,1,3)

plt.scatter(full_data[“distance to primary school in km”],full_data[“Average house

plt.xlabel(“distance to primary school in km”)数据分析代写

plt.show()

Modeling 数据分析代写

XGBoost

In [50]:

model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,

learning_rate=0.05, max_depth=3,

min_child_weight=1.7817, n_estimators=2200,

reg_alpha=0.4640, reg_lambda=0.8571,

subsample=0.5213, silent=1,

random_state =7, nthread = –1)数据分析代写

set up cross validation function

In [51]:

#Validation function

n_folds = 5

def rmsle_cv(model):

kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(full_data.dro

rmse= np.sqrt(–cross_val_score(model, full_data.drop([“Average house price *10

return(rmse)数据分析代写

In [52]:

score = rmsle_cv(model_xgb)

print(“Xgboost score: {:.4f} ({:.4f})\n”.format(score.mean(), score.std()))

Xgboost score: 0.1370 (0.0258)

Here, we find out the error is actually smaller than that of housing data.

In [54]:

model_xgb.fit(full_data.drop([“Average house price *1000”],axis = 1),full_data[“A

Out[54]: XGBRegressor(base_score=0.5, booster=’gbtree’, colsample_bylevel=1, colsample_bytree=0.4603, gamma=0.0468, learning_rate=0.05, max_delta_step=0, max_depth=3, min_child_weight=1.7817, missing=None, n_estimators=2200, n_jobs=1, nthread=-1, objective=’reg:linear’, random_state=7, reg_alpha=0.464, reg_lambda=0.8571, scale_pos_weight=1, seed=None, silent=1, subsample=0.5213)数据分析代写

Similarly, we will plot the ten most important features

In [55]:

xgb.plot_importance(model_xgb,max_num_features = 10)

Out[55]: <matplotlib.axes._subplots.AxesSubplot at 0x23cf9669748>

Support vector regression 数据分析代写

In [56]:

from sklearn import svm

model_svr = svm.SVR()

score = rmsle_cv(model_svr)

print(“rmse of support vector regression is {}, standard error is {}”.format(score

rmse of support vector regression is 0.2375679885077536, standard error is 0.02 6084243485418934

Neural Network

In [57]:

from keras import Sequential

model = Sequential()

from keras.layers import Dense

model.add(Dense(units=16, activation=‘relu’, input_dim=22))

model.add(Dense(units=16, activation=‘linear’, input_dim=16))数据分析代写

model.add(Dense(units=1, activation=‘relu’, input_dim=16))

model.compile(loss=‘mean_squared_error’, optimizer=‘sgd’, metrics=[‘accuracy’])

model.fit(x=full_data.drop([“Average house price *1000”],axis = 1), y=full_data[“A

Train on 342 samples, validate on 39 samples Epoch 1/5

342/342 [==============================] – 0s 491us/step – loss: nan – acc: 0.0

000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 2/5数据分析代写

342/342 [==============================] – 0s 79us/step – loss: nan – acc: 0.00

00e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 3/5

342/342 [==============================] – 0s 117us/step – loss: nan – acc: 0.0

000e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 4/5

342/342 [==============================] – 0s 82us/step – loss: nan – acc: 0.00

00e+00 – val_loss: nan – val_acc: 0.0000e+00 Epoch 5/5数据分析代写

342/342 [==============================] – 0s 85us/step – loss: nan – acc: 0.00

00e+00 – val_loss: nan – val_acc: 0.0000e+00 Out[57]: <keras.callbacks.History at 0x23cf9870668>

Similary, with so little data, it’s really hard to train a neural network.

Conclusion

Again, we find that xgboost performs best among three models. And for full_data, the overall error is smaller than that of housing dataset. I guess that’s because full_data dataset is an average of all house in this area, error is more likely to cancel out each other. Pitersonally, I prefer to use xgboosgt in this scenario, not only because gradient boosing machine product the least error here, but it also

provides an interpretation of the data. For example, for full_data dataset, xgboost reveals that average house income, population and distance to cinema are the three most important factors in affecting housing price. Neural network can not perform well in this scenario, because the number of parameters in neural network is even comparable to number of samples.

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合作平台：天才代写幽灵代写写手招聘 Essay代写

A data analysis of Holland housing price

Part 1: housing data 数据分析代写

Dealing with missing values

Sale Price 数据分析代写

House type 数据分析代写

YEAR BUILD 数据分析代写

Living area 数据分析代写

External storage space data analysis1代写

Plot area

Volume 数据分析代写

total rooms数据分析代写

sleeprooms

bathrooms 数据分析代写

separate toilet 数据分析代写

Number of floors\n

EERGY LABELn

Ownership situation

Backyard 数据分析代写

Barn storage

Adres, Postcode数据分析代写

Exploratory data analysis

correlation heat map between numerical values

Modeling

XGBoost

neural network

Final conclusion

Part 2: full data数据分析代写

Missing values

Exploratory data analysis

correlation heatmap

scatterplot with respect to highly correlated variables

Modeling 数据分析代写

XGBoost

Support vector regression 数据分析代写

Neural Network

Conclusion

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