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House Price Prediction using Machine Learning in Python

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  • Difficulty Level : Medium
  • Last Updated : 07 Sep, 2022
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We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on.

House Price Prediction using Machine Learning

So to deal with this kind of issues Today we will be preparing a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset. 

You can download the dataset from this link.

The dataset contains 13 features :

1 Id To count the records.
2 MSSubClass  Identifies the type of dwelling involved in the sale.
3 MSZoning Identifies the general zoning classification of the sale.
4 LotArea  Lot size in square feet.
5 LotConfig Configuration of the lot
6 BldgType Type of dwelling
7 OverallCond Rates the overall condition of the house
8 YearBuilt Original construction year
9 YearRemodAdd Remodel date (same as construction date if no remodeling or additions).
10 Exterior1st Exterior covering on house
11 BsmtFinSF2 Type 2 finished square feet.
12 TotalBsmtSF Total square feet of basement area
13 SalePrice To be predicted

Importing Libraries and Dataset

Here we are using 

  • Pandas To load the Dataframe
  • Matplotlib To visualize the data features i.e. barplot
  • Seaborn To see the correlation between features using heatmap

Python3




import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
 
dataset = pd.read_excel("HousePricePrediction.xlsx")
 
# Printing first 5 records of the dataset
print(dataset.head(5))


Output:

 

As we have imported the data. So shape method will show us the dimension of the dataset. 

Python3




dataset.shape


Output: 

(2919,13)

Data Preprocessing

Now, we categorize the features depending on their datatype (int, float, object) and then calculate the number of them. 

Python3




obj = (dataset.dtypes == 'object')
object_cols = list(obj[obj].index)
print("Categorical variables:",len(object_cols))
 
int_ = (dataset.dtypes == 'int')
num_cols = list(int_[int_].index)
print("Integer variables:",len(num_cols))
 
fl = (dataset.dtypes == 'float')
fl_cols = list(fl[fl].index)
print("Float variables:",len(fl_cols))


Output: 

Categorical variables : 4
Integer variables : 6
Float variables : 3

Exploratory Data Analysis

EDA refers to the deep analysis of data so as to discover different patterns and spot anomalies. Before making inferences from data it is essential to examine all your variables.

So here let’s make a heatmap using seaborn library.

Python3




plt.figure(figsize=(12, 6))
sns.heatmap(dataset.corr(),
            cmap = 'BrBG',
            fmt = '.2f',
            linewidths = 2,
            annot = True)


Output:

 

To analyze the different categorical features. Let’s draw the barplot.

Python3




unique_values = []
for col in object_cols:
  unique_values.append(dataset[col].unique().size)
plt.figure(figsize=(10,6))
plt.title('No. Unique values of Categorical Features')
plt.xticks(rotation=90)
sns.barplot(x=object_cols,y=unique_values)


Output:

 

The plot shows that Exterior1st has around 16 unique categories and other features have around  6 unique categories. To findout the actual count of each category we can plot the bargraph of each four features separately.

Python3




plt.figure(figsize=(18, 36))
plt.title('Categorical Features: Distribution')
plt.xticks(rotation=90)
index = 1
 
for col in object_cols:
    y = dataset[col].value_counts()
    plt.subplot(11, 4, index)
    plt.xticks(rotation=90)
    sns.barplot(x=list(y.index), y=y)
    index += 1


Output:

 

Data Cleaning

Data Cleaning is the way to improvise the data or remove incorrect, corrupted or irrelevant data.

As in our dataset, there are some columns that are not important and irrelevant for the model training. So, we can drop that column before training. There are 2 approaches to dealing with empty/null values

  • We can easily delete the column/row (if the feature or record is not much important).
  • Filling the empty slots with mean/mode/0/NA/etc. (depending on the dataset requirement).

As Id Column will not be participating in any prediction. So we can Drop it.

Python3




dataset.drop(['Id'],
             axis=1,
             inplace=True)


Replacing SalePrice empty values with their mean values to make the data distribution symmetric.

Python3




dataset['SalePrice'] = dataset['SalePrice'].fillna(
  dataset['SalePrice'].mean())


Drop records with null values (as the empty records are very less).

Python3




new_dataset = dataset.dropna()


Checking features which have null values in the new dataframe (if there are still any).

Python3




new_dataset.isnull().sum()


Output:

 

OneHotEncoder – For Label categorical features

One hot Encoding is the best way to convert categorical data into binary vectors. This maps the values to integer values. By using OneHotEncoder, we can easily convert object data into int. So for that, firstly we have to collect all the features which have the object datatype. To do so, we will make a loop.

Python3




from sklearn.preprocessing import OneHotEncoder
 
s = (new_dataset.dtypes == 'object')
object_cols = list(s[s].index)
print("Categorical variables:")
print(object_cols)
print('No. of. categorical features: ',
      len(object_cols))


Output:

 

Then once we have a list of all the features. We can apply OneHotEncoding to the whole list.

Python3




OH_encoder = OneHotEncoder(sparse=False)
OH_cols = pd.DataFrame(OH_encoder.fit_transform(new_dataset[object_cols]))
OH_cols.index = new_dataset.index
OH_cols.columns = OH_encoder.get_feature_names()
df_final = new_dataset.drop(object_cols, axis=1)
df_final = pd.concat([df_final, OH_cols], axis=1)


Splitting Dataset into Training and Testing

X and Y splitting (i.e. Y is the SalePrice column and the rest of the other columns are X)

Python3




from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
 
X = df_final.drop(['SalePrice'], axis=1)
Y = df_final['SalePrice']
 
# Split the training set into
# training and validation set
X_train, X_valid, Y_train, Y_valid = train_test_split(
    X, Y, train_size=0.8, test_size=0.2, random_state=0)


Model and Accuracy

As we have to train the model to determine the continuous values, so we will be using these regression models.

  • SVM-Support Vector Machine
  • Random Forest Regressor
  • Linear Regressor

And To calculate loss we will be using the mean_absolute_percentage_error module. It can easily be imported by using sklearn library. The formula for Mean Absolute Error : 

 

SVM – Support vector Machine

SVM can be used for both regression and classification model. It finds the hyperplane in the n-dimensional plane. To read more about svm refer this.

Python3




from sklearn import svm
from sklearn.svm import SVC
from sklearn.metrics import mean_absolute_percentage_error
 
model_SVR = svm.SVR()
model_SVR.fit(X_train,Y_train)
Y_pred = model_SVR.predict(X_valid)
 
print(mean_absolute_percentage_error(Y_valid, Y_pred))


Output : 

0.18705129

Random Forest Regression

Random Forest is an ensemble technique that uses multiple of decision trees and can be used for both regression and classification tasks. To read more about random forests refer this.

Python3




from sklearn.ensemble import RandomForestRegressor
 
model_RFR = RandomForestRegressor(n_estimators=10)
model_RFR.fit(X_train, Y_train)
Y_pred = model_RFR.predict(X_valid)
 
mean_absolute_percentage_error(Y_valid, Y_pred)


Output : 

0.1929469

Linear Regression

Linear Regression predicts the final output-dependent value based on the given independent features. Like, here we have to predict SalePrice depending on features like MSSubClass, YearBuilt, BldgType, Exterior1st etc. To read more about Linear Regression refer this.

Python3




from sklearn.linear_model import LinearRegression
 
model_LR = LinearRegression()
model_LR.fit(X_train, Y_train)
Y_pred = model_LR.predict(X_valid)
 
print(mean_absolute_percentage_error(Y_valid, Y_pred))


Output : 

0.187416838

Conclusion 

Clearly, SVM model is giving better accuracy as the mean absolute error is the least among all the other regressor models i.e. 0.18 approx. To get much better results ensemble learning techniques like Bagging and Boosting can also be used.


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