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 pdimport matplotlib.pyplot as pltimport seaborn as snsdataset = pd.read_excel("HousePricePrediction.xlsx")# Printing first 5 records of the datasetprint(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 = 1for 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 OneHotEncoders = (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.indexOH_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_errorfrom sklearn.model_selection import train_test_splitX = df_final.drop(['SalePrice'], axis=1)Y = df_final['SalePrice']# Split the training set into# training and validation setX_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 svmfrom sklearn.svm import SVCfrom sklearn.metrics import mean_absolute_percentage_errormodel_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 RandomForestRegressormodel_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 LinearRegressionmodel_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
CatBoost Classifier
CatBoost is a machine learning algorithm implemented by Yandex and is open-source. It is simple to interface with deep learning frameworks such as Apple’s Core ML and Google’s TensorFlow. Performance, ease-of-use, and robustness are the main advantages of the CatBoost library. To read more about CatBoost refer this.
Python3
# This code is contributed by @amartajiscefrom catboost import CatBoostRegressorcb_model = CatBoostRegressor()cb_model.fit(X_train, y_train)preds = cb_model.predict(X_valid)cb_r2_score=r2_score(Y_valid, preds)cb_r2_score |
0.893643437976127
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.
