Housing data analyses🏠🪴

[MaherAssaf19]

This script aims to predict housing prices based on features like the size of the house, number of bedrooms, bathrooms, year built, and location score. Here's a simplified breakdown of the process:

1. Loading the Data: It loads a small dataset containing information about different houses.
2. Cleaning the Data: Any missing values are removed to ensure the data is ready for analysis.
3. Exploratory Data Analysis (EDA): The script provides a quick look at the data with summary statistics and visual plots to understand how the features relate to the price.
4. Training the Model: A linear regression model is trained to learn the relationship between the features and the house price.
5. Evaluating the Model: The model's accuracy is checked using metrics like Mean Squared Error and R-squared.
6. Visualizing Results: The script compares the actual prices to the predicted ones and shows which features matter most in determining the price.

In short, this process builds a predictive model that estimates house prices and helps identify what factors most influence those prices.

Here is the code: 👇

housing_prices_analysis.py

def load_data():
"""
Load a predefined housing dataset.

Returns:
    pd.DataFrame: Loaded dataset as a Pandas DataFrame.
"""
import pandas as pd
from io import StringIO

# Embedded dataset
data = """SquareFeet,Bedrooms,Bathrooms,YearBuilt,LocationScore,Price

1500,3,2,2000,85,300000
2000,4,3,2010,90,450000
1800,3,2,2005,88,350000
2400,4,3,2020,92,500000
1600,3,2,1995,80,280000
1200,2,1,1980,70,200000
"""
return pd.read_csv(StringIO(data))

def preprocess_data(data):
"""
Preprocess the housing dataset by handling missing values and extracting necessary features.

Parameters:
    data (pd.DataFrame): Raw dataset.

Returns:
    pd.DataFrame: Preprocessed dataset.
"""
data = data.dropna()
return data

def analyze_data(data):
"""
Perform exploratory data analysis on the dataset.

Parameters:
    data (pd.DataFrame): Dataset to analyze.

Returns:
    None: Prints summary statistics and shows plots.
"""
import matplotlib.pyplot as plt
import seaborn as sns

print("Dataset Summary:")
print(data.describe())

sns.pairplot(data[['SquareFeet', 'Bedrooms', 'Bathrooms', 'YearBuilt', 'LocationScore', 'Price']])
plt.show()

def train_model(data):
"""
Train a predictive model using the dataset.

Parameters:
    data (pd.DataFrame): Preprocessed dataset.

Returns:
    model: Trained model.
    X_test (pd.DataFrame): Test features.
    y_test (pd.Series): Test target values.
"""
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

features = ['SquareFeet', 'Bedrooms', 'Bathrooms', 'YearBuilt', 'LocationScore']
target = 'Price'

X = data[features]
y = data[target]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

return model, X_test, y_test

def evaluate_model(model, X_test, y_test):
"""
Evaluate the trained model using Mean Squared Error and R-squared metrics.

Parameters:
    model: Trained model.
    X_test (pd.DataFrame): Test features.
    y_test (pd.Series): Test target values.

Returns:
    None: Prints evaluation metrics.
"""
from sklearn.metrics import mean_squared_error, r2_score

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")

def visualize_results(model, X_test, y_test):
"""
Visualize the actual vs predicted prices and feature importance.

Parameters:
    model: Trained model.
    X_test (pd.DataFrame): Test features.
    y_test (pd.Series): Test target values.

Returns:
    None: Displays plots.
"""
import matplotlib.pyplot as plt
import pandas as pd

# Actual vs Predicted Prices
y_pred = model.predict(X_test)
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, alpha=0.6, color='blue')
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--r', linewidth=2)
plt.xlabel('Actual Prices')
plt.ylabel('Predicted Prices')
plt.title('Actual vs Predicted Prices')
plt.show()

# Feature Coefficients
coefficients = pd.Series(model.coef_, index=X_test.columns)
plt.figure(figsize=(8, 4))
coefficients.plot(kind='bar', color='skyblue')
plt.title('Feature Coefficients')
plt.ylabel('Coefficient Value')
plt.show()

Example Usage

if name == "main":
data = load_data()
data = preprocess_data(data)
analyze_data(data)
model, X_test, y_test = train_model(data)
evaluate_model(model, X_test, y_test)
visualize_results(model, X_test, y_test)

[MaRia19280]

This script is an excellent end-to-end solution for predicting housing prices, covering data cleaning, EDA, model training, evaluation, and visualization. Its structured workflow ensures reliability, while linear regression provides interpretability. Adding cross-validation, handling outliers, or testing advanced models could further enhance performance. Overall, it's a solid foundation for real estate analytics. Well put, Maher 😊

[MuhannadGTR]

Hi Maher,

Please follow the instructions for adding your file.