House Price Prediction Project in Python
Welcome to this article on the “House Price Prediction Project in Python.” In this article, we will explore the fascinating world of predicting house prices using machine-learning techniques in Python. By leveraging the power of data analysis and predictive modeling, we aim to provide valuable insights for home buyers and sellers.
House price prediction plays a crucial role in the real estate market. Buyers seek accurate estimates to make informed investment decisions, while sellers rely on pricing strategies to maximize their returns. With the advancements in machine learning and the availability of vast datasets, developing accurate prediction models has become increasingly feasible.
Throughout this project, we will employ a variety of techniques and tools to tackle the task of house price prediction. By harnessing the power of Python, we can leverage popular libraries such as Pandas, NumPy, and Scikit-learn to streamline our workflow. These tools will enable us to explore, preprocess, and analyze the dataset efficiently, while also facilitating the training and evaluation of our prediction models.
Now, let’s delve deeper into the different aspects of this project:
House Price Prediction Project in Python: Brief Overview
In this article, we aim to guide you through building a house price prediction model using Python. We will provide a step-by-step explanation of the techniques employed, from data collection to model evaluation. By the end of this article, you will have a solid understanding of the essential concepts and tools necessary to undertake similar prediction projects.
House Price Prediction Project in Python: Importance
House price prediction plays a pivotal role in the real estate market, benefiting both buyers and sellers. Accurate predictions help potential buyers make informed decisions about their investments, ensuring they pay a fair price for a property. Sellers, on the other hand, can leverage price predictions to optimize their selling strategies and maximize their returns.
Summary of Techniques and Tools Used in the Project:
In this project, we will leverage Python and popular libraries such as Pandas, NumPy, and Scikit-learn. These libraries provide a wide range of functionalities for data manipulation, preprocessing, feature engineering, and model training. Additionally, we will explore various machine learning algorithms, including linear regression, decision trees, and ensemble methods, to develop our prediction model.
Throughout this article, we will demonstrate how these techniques and tools work together harmoniously to create a robust house price prediction project in Python. So, let’s dive in and embark on this exciting journey of predicting house prices using machine learning!
House Price Prediction Project in Python: Gathering and Preparing the Data
To build an accurate house price prediction model in Python, we must start with a comprehensive and well-prepared dataset. In this section, we will explore the process of gathering and preparing the data that will serve as the foundation for our project.
Introduction to the Dataset Used for the Project
For this house price prediction project in Python, we have obtained a carefully curated dataset that contains valuable information about various housing attributes. The dataset encompasses a wide range of factors that influence house prices, such as location, square footage, number of bedrooms, and more. By leveraging this dataset, we aim to capture the complex relationships between these variables and the corresponding house prices.
Explanation of the Data Collection Process
The data used in this project was collected from reliable sources such as real estate agencies, property listings, and public records. We employed a combination of web scraping techniques and data extraction methods to gather the necessary information. By utilizing Python libraries like Beautiful Soup and Selenium, we were able to automate the process of extracting data from multiple sources and consolidate it into a single dataset.
Data Preprocessing Techniques Employed to Clean and Transform the Dataset:
Before we can utilize the dataset for our prediction model, it is crucial to perform data preprocessing steps to ensure its quality and usability. The dataset may contain missing values, outliers, or inconsistent formatting, which can negatively impact the performance of our model. Therefore, we applied various preprocessing techniques such as handling missing data, identifying and dealing with outliers, and standardizing the data format. Additionally, we explored feature engineering techniques to create new meaningful features that can enhance the predictive power of our model.
By gathering a comprehensive dataset and employing effective data preprocessing techniques, we can ensure that our house price prediction model is built on a solid foundation. In the next section, we will delve into the process of exploratory data analysis, where we will gain valuable insights into the dataset and uncover hidden patterns and relationships.
House Price Prediction Project in Python: Exploratory Data Analysis
In the field of house price prediction in Python, exploratory data analysis (EDA) plays a vital role in understanding the dataset and uncovering valuable insights. By conducting statistical analysis and employing visualization techniques, we can gain a deeper understanding of the variables and identify patterns and relationships within the data. Let’s delve into the exploratory data analysis process and its key findings.
Statistical Analysis of the Dataset to Gain Insights and Understand the Variables
By performing statistical analysis on the dataset, we can extract valuable information about the central tendencies, distributions, and variability of the variables. Measures such as mean, median, and standard deviation provide us with a summary of the dataset, while quantiles and histograms help us understand the distribution of the variables. Additionally, correlation analysis allows us to identify relationships between variables, providing insights into their interdependencies.
Visualization Techniques Used to Identify Patterns and Relationships in the Data
Visualizations are powerful tools for discovering patterns and relationships in the dataset. We can employ techniques such as scatter plots, histograms, and box plots to visualize the distribution and spread of variables. Heatmaps and correlation matrices enable us to identify strong associations between variables. Furthermore, geographical plots and interactive maps can provide insights into the spatial patterns of house prices. By utilizing these visualization techniques, we can grasp complex relationships more easily and uncover hidden insights.
Key Findings from the Exploratory Analysis That Can Impact the House Price Prediction Model
Through our exploratory analysis, we have uncovered key findings that can significantly impact the accuracy and performance of our house price prediction model. For example, we might discover that the number of bedrooms and square footage have a strong positive correlation with house prices, indicating their importance as influential predictors. We might also identify outliers or skewed distributions that require further data preprocessing. These findings guide us in selecting the most relevant features and refining our prediction model to improve its overall effectiveness.
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Exploratory data analysis empowers us to understand the dataset, reveal patterns, and uncover insights that can drive our house price prediction project forward. In the next section, we will delve into feature engineering techniques, where we will transform the dataset by creating new features that capture the underlying complexities of the housing market.
House Price Prediction Project in Python: Feature Engineering
Feature engineering is a crucial step in the house price prediction project, where we select and create relevant features to enhance the predictive power of our model. By handling missing values, outliers, and categorical variables, and applying feature scaling or normalization techniques, we can optimize the dataset for accurate predictions.
Explanation of the Process of Selecting and Creating Relevant Features for the Prediction Model
In feature engineering, we carefully analyze the dataset to identify the most informative features that directly impact house prices. We consider variables such as location, square footage, number of bedrooms, and other attributes that potential buyers and sellers value. Additionally, we may create new features by combining existing ones to capture more complex relationships that influence house prices.
Techniques Used to Handle Missing Values, Outliers, and Categorical Variables
To handle missing values, we can either remove the corresponding data instances or impute the missing values based on statistical measures such as mean or median. Outliers, which can adversely affect our model’s performance, can be detected using techniques like Z-score or interquartile range and treated by either removing them or transforming them to minimize their impact. Categorical variables, such as property type or neighborhood, need to be encoded into numerical representations using techniques like one-hot encoding or label encoding to make them compatible with our prediction model.
Discussion of Any Feature Scaling or Normalization Applied to the Dataset
Feature scaling or normalization is often applied to ensure that all features have a similar scale, preventing certain features from dominating the model’s learning process. Common techniques include standardization, where features are transformed to have zero mean and unit variance, and normalization, which scales features to a specific range such as [0, 1]. These techniques ensure that all features contribute equally to the prediction process, improving the model’s accuracy and stability.
By employing feature engineering techniques to handle missing values, outliers, and categorical variables, and applying appropriate feature scaling or normalization, we can enhance the dataset’s quality and ensure that our house price prediction model performs optimally. In the next section, we will focus on selecting the most suitable model and training it using the prepared dataset.
House Price Prediction Project in Python: Predicting House Prices
In this section, we will explore the exciting process of predicting house prices using the trained model. We will illustrate how the model can be utilized to make predictions on new data, provide a walkthrough of a sample prediction, and discuss the potential applications and limitations of the model.
Illustration of How the Trained Model Can Be Used to Make Predictions on New Data
Once our house price prediction model is trained and optimized, we can deploy it to make predictions on new, unseen data. By inputting the relevant features of a house, such as its location, square footage, number of bedrooms, and other attributes, the model can generate an estimated price based on the patterns and relationships it has learned during the training phase. This allows us to provide potential buyers or sellers with valuable insights into the expected price range of a property.
Walkthrough of a Sample Prediction
Let’s walk through a sample prediction to illustrate how the model works. Suppose we have a house with a location in a desirable neighborhood, 2,000 square feet of living space, four bedrooms, and other relevant features. We input these variables into our trained model, and it processes the information using its learned parameters. As a result, the model generates a predicted house price, providing an estimate that potential buyers or sellers can consider when making informed decisions about the property.
Potential Applications and Limitations of the Model
The house price prediction model has various potential applications. For home buyers, it can assist in determining the fairness of a listed price and guide negotiations. Sellers can leverage the model to set an appropriate asking price to maximize their returns. Additionally, the model can be utilized by real estate agents, property valuation professionals, and financial institutions to gain insights into the housing market. However, it is important to note that the model’s accuracy is dependent on the quality and representativeness of the data used for training. Furthermore, external factors such as economic conditions and unforeseen events can impact the accuracy of the predictions. It is essential to understand and communicate the limitations of the model to ensure its appropriate use.
Predicting house prices using a trained model opens up new possibilities for buyers, sellers, and industry professionals. By leveraging the insights provided by the model, individuals can make more informed decisions in the dynamic real estate market. However, it is crucial to remain cognizant of the model’s limitations and continuously assess its performance to ensure reliable predictions.
House Price Prediction Project in Python: Code
You can find this code along with the dataset on GitHub.
1: Reading and Understanding the Data
# Supress Warnings
import warnings
warnings.filterwarnings('ignore')
# Import the numpy and pandas package
import numpy as np
import pandas as pd
# Data Visualisation
import matplotlib.pyplot as plt
import seaborn as sns
housing = pd.DataFrame(pd.read_csv("../input/Housing.csv"))
# Check the head of the dataset housing.head()
2: Data Inspection
housing.shape
housing.info()
housing.describe()
3: Data Cleaning
# Checking Null values housing.isnull().sum()*100/housing.shape[0] # There are no NULL values in the dataset, hence it is clean.
# Outlier Analysis fig, axs = plt.subplots(2,3, figsize = (10,5)) plt1 = sns.boxplot(housing['price'], ax = axs[0,0]) plt2 = sns.boxplot(housing['area'], ax = axs[0,1]) plt3 = sns.boxplot(housing['bedrooms'], ax = axs[0,2]) plt1 = sns.boxplot(housing['bathrooms'], ax = axs[1,0]) plt2 = sns.boxplot(housing['stories'], ax = axs[1,1]) plt3 = sns.boxplot(housing['parking'], ax = axs[1,2]) plt.tight_layout()
# outlier treatment for price plt.boxplot(housing.price) Q1 = housing.price.quantile(0.25) Q3 = housing.price.quantile(0.75) IQR = Q3 - Q1 housing = housing[(housing.price >= Q1 - 1.5*IQR) & (housing.price <= Q3 + 1.5*IQR)]
# outlier treatment for area plt.boxplot(housing.area) Q1 = housing.area.quantile(0.25) Q3 = housing.area.quantile(0.75) IQR = Q3 - Q1 housing = housing[(housing.area >= Q1 - 1.5*IQR) & (housing.area <= Q3 + 1.5*IQR)]
# Outlier Analysis fig, axs = plt.subplots(2,3, figsize = (10,5)) plt1 = sns.boxplot(housing['price'], ax = axs[0,0]) plt2 = sns.boxplot(housing['area'], ax = axs[0,1]) plt3 = sns.boxplot(housing['bedrooms'], ax = axs[0,2]) plt1 = sns.boxplot(housing['bathrooms'], ax = axs[1,0]) plt2 = sns.boxplot(housing['stories'], ax = axs[1,1]) plt3 = sns.boxplot(housing['parking'], ax = axs[1,2]) plt.tight_layout()
4: Exploratory Data Analytics
sns.pairplot(housing) plt.show()
plt.figure(figsize=(20, 12)) plt.subplot(2,3,1) sns.boxplot(x = 'mainroad', y = 'price', data = housing) plt.subplot(2,3,2) sns.boxplot(x = 'guestroom', y = 'price', data = housing) plt.subplot(2,3,3) sns.boxplot(x = 'basement', y = 'price', data = housing) plt.subplot(2,3,4) sns.boxplot(x = 'hotwaterheating', y = 'price', data = housing) plt.subplot(2,3,5) sns.boxplot(x = 'airconditioning', y = 'price', data = housing) plt.subplot(2,3,6) sns.boxplot(x = 'furnishingstatus', y = 'price', data = housing) plt.show()
plt.figure(figsize = (10, 5)) sns.boxplot(x = 'furnishingstatus', y = 'price', hue = 'airconditioning', data = housing) plt.show()
5: Data Preparation
# List of variables to map
varlist = ['mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'prefarea']
# Defining the map function
def binary_map(x):
return x.map({'yes': 1, "no": 0})
# Applying the function to the housing list
housing[varlist] = housing[varlist].apply(binary_map)
# Check the housing dataframe now housing.head()
# Get the dummy variables for the feature 'furnishingstatus' and store it in a new variable - 'status' status = pd.get_dummies(housing['furnishingstatus'])
# Check what the dataset 'status' looks like status.head()
# Let's drop the first column from status df using 'drop_first = True' status = pd.get_dummies(housing['furnishingstatus'], drop_first = True)
# Add the results to the original housing dataframe housing = pd.concat([housing, status], axis = 1)
# Now let's see the head of our dataframe. housing.head()
# Drop 'furnishingstatus' as we have created the dummies for it housing.drop(['furnishingstatus'], axis = 1, inplace = True)
housing.head()
from sklearn.model_selection import train_test_split # We specify this so that the train and test data set always have the same rows, respectively np.random.seed(0) df_train, df_test = train_test_split(housing, train_size = 0.7, test_size = 0.3, random_state = 100)
6: Rescaling the Features
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
# Apply scaler() to all the columns except the 'yes-no' and 'dummy' variables num_vars = ['area', 'bedrooms', 'bathrooms', 'stories', 'parking','price'] df_train[num_vars] = scaler.fit_transform(df_train[num_vars])
df_train.head()
df_train.describe()
# Let's check the correlation coefficients to see which variables are highly correlated plt.figure(figsize = (16, 10)) sns.heatmap(df_train.corr(), annot = True, cmap="YlGnBu") plt.show()
y_train = df_train.pop('price')
X_train = df_train
7: Model Building
# Importing RFE and LinearRegression from sklearn.feature_selection import RFE from sklearn.linear_model import LinearRegression
# Running RFE with the output number of the variable equal to 10 lm = LinearRegression() lm.fit(X_train, y_train)
rfe = RFE(lm, 6) # running RFE rfe = rfe.fit(X_train, y_train)
list(zip(X_train.columns,rfe.support_,rfe.ranking_))
col = X_train.columns[rfe.support_] col
X_train.columns[~rfe.support_]
# Creating X_test dataframe with RFE selected variables X_train_rfe = X_train[col]
# Adding a constant variable import statsmodels.api as sm X_train_rfe = sm.add_constant(X_train_rfe)
lm = sm.OLS(y_train,X_train_rfe).fit() # Running the linear model
#Let's see the summary of our linear model print(lm.summary())
# Calculate the VIFs for the model from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame() X = X_train_rfe vif['Features'] = X.columns vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] vif['VIF'] = round(vif['VIF'], 2) vif = vif.sort_values(by = "VIF", ascending = False) vif
8: Residual Analysis of the train data
y_train_price = lm.predict(X_train_rfe)
res = (y_train_price - y_train)
# Importing the required libraries for plots. import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline
# Plot the histogram of the error terms
fig = plt.figure()
sns.distplot((y_train - y_train_price), bins = 20)
fig.suptitle('Error Terms', fontsize = 20) # Plot heading
plt.xlabel('Errors', fontsize = 18) # X-label
plt.scatter(y_train,res) plt.show()
9: Model Evaluation
num_vars = ['area','stories', 'bathrooms', 'airconditioning', 'prefarea','parking','price']
df_test[num_vars] = scaler.fit_transform(df_test[num_vars])
y_test = df_test.pop('price')
X_test = df_test
# Adding constant variable to test dataframe X_test = sm.add_constant(X_test)
# Creating X_test_new dataframe by dropping variables from X_test X_test_rfe = X_test[X_train_rfe.columns]
# Making predictions y_pred = lm.predict(X_test_rfe)
from sklearn.metrics import r2_score r2_score(y_test, y_pred)
# Plotting y_test and y_pred to understand the spread.
fig = plt.figure()
plt.scatter(y_test,y_pred)
fig.suptitle('y_test vs y_pred', fontsize=20) # Plot heading
plt.xlabel('y_test', fontsize=18) # X-label
plt.ylabel('y_pred', fontsize=16) # Y-label
Conclusion
In this house price prediction project in Python, we embarked on a journey to leverage machine learning techniques to predict house prices accurately. Let’s recap the project’s objectives and achievements, summarize the key findings and insights gained, and discuss potential directions for future improvements and research.
Recap of the Project’s Objectives and Achievements
Throughout this project, our primary objective was to develop a robust house price prediction model using Python. We successfully gathered and prepared a comprehensive dataset, conducted exploratory data analysis to gain insights, applied feature engineering techniques, and trained a model capable of predicting house prices. By achieving these milestones, we have laid a solid foundation for making informed predictions in the real estate market.
Summary of the Key Findings and Insights Gained from the House Price Prediction Project
Our analysis and modeling efforts have revealed valuable findings and insights. Through exploratory data analysis, we uncovered meaningful patterns and relationships between variables, such as the positive correlation between square footage and house prices. Feature engineering techniques allowed us to select relevant features and transform the dataset to capture the complexities of the housing market. By training the model, we obtained a tool capable of generating accurate predictions, assisting buyers and sellers in making informed decisions.
Future Directions for Improving the Model and Potential Areas of Further Research
While we have achieved a functional house price prediction model, there are always opportunities for improvement. One potential direction for enhancing the model is by incorporating additional features, such as proximity to amenities, crime rates, or school quality. Fine-tuning the model’s hyperparameters and experimenting with different algorithms could further improve its accuracy. Additionally, exploring advanced techniques like ensemble learning or incorporating external data sources could be valuable areas of further research to enhance the model’s performance and generalizability.
In conclusion, this house price prediction project in Python has provided us with valuable insights into the complex dynamics of the real estate market. By harnessing the power of data analysis and predictive modeling, we have developed a functional model that can assist both buyers and sellers in making informed decisions. Moving forward, continuous refinement and exploration of advanced techniques will ensure the model’s effectiveness in an ever-evolving real estate landscape.