Real Estate Web App
AI-powered property insights and smart recommendations
The Challenge
Property buyers often struggle with price estimation, relevant recommendations, and market analysis. Traditional real estate platforms lack AI-driven insights, making decision-making complex and time-consuming.
The Solution
We built an AI-powered real estate web app with property price prediction, personalized recommendations, and interactive analytics. The platform streamlines property searches, enhances decision-making, and provides data-driven insights for buyers and investors.
Tech Mastery Showcase
StreamlitUsed for building an interactive and user-friendly web interface, allowing users to input property details and view predictions in real-time.
PythonEmployed for data processing, machine learning model development, and backend logic implementation.
PandasUtilized for data manipulation and analysis, ensuring clean and structured input data for the model.
Scikit-learnImplemented for training and evaluating machine learning models, providing accurate price predictions.
NumPyUsed for numerical computations and handling large datasets efficiently.
PickleIntegrated for saving and loading trained machine learning models, ensuring quick deployment and updates.
Innovative Logic & Implementation
Data Preprocessing and Feature Engineering
Performed extensive data cleaning and feature engineering to prepare the dataset for model training. This included handling missing values, encoding categorical variables, and scaling numerical features.
1import pandas as pd
2 import numpy as np
3
4 # Load dataset
5 df = pd.read_pickle('df.pkl')
6
7 # Handle missing values
8 df.fillna(method='ffill', inplace=True)
9
10 # Encode categorical variables
11 df = pd.get_dummies(df, columns=['property_type', 'sector'])
12
13 # Scale numerical features
14 from sklearn.preprocessing import StandardScaler
15 scaler = StandardScaler()
16 df[['area', 'bedrooms', 'bathrooms']] = scaler.fit_transform(df[['area', 'bedrooms', 'bathrooms']])Machine Learning Model Training
Trained a regression model to predict property prices based on the processed dataset. The model was evaluated using cross-validation and fine-tuned for optimal performance.
1from sklearn.model_selection import train_test_split
2 from sklearn.ensemble import RandomForestRegressor
3 from sklearn.metrics import mean_absolute_error
4
5 # Split dataset into training and testing sets
6 X = df.drop('price', axis=1)
7 y = df['price']
8 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
9
10 # Train the model
11 model = RandomForestRegressor(n_estimators=100, random_state=42)
12 model.fit(X_train, y_train)
13
14 # Evaluate the model
15 predictions = model.predict(X_test)
16 mae = mean_absolute_error(y_test, predictions)
17 print(f'Mean Absolute Error: {mae}')Overcoming Challenges
Building an Accurate Price Prediction Model
Predicting real estate prices is challenging due to fluctuating market conditions and diverse influencing factors.
Solution:
Used feature engineering techniques and ensemble models (Random Forest, XGBoost) to improve prediction accuracy.
Optimizing Recommendation System Performance
Personalized property recommendations require processing large datasets efficiently.
Solution:
Implemented collaborative filtering and content-based filtering with optimized query indexing.
Creating a User-Friendly Visualization Dashboard
Presenting complex real estate data in an intuitive and digestible format was crucial.
Solution:
Designed an interactive UI with Streamlit, incorporating filters, graphs, and comparative analysis tools.
Key Learnings & Growth
- 🚀
Gained expertise in machine learning models for real estate price prediction.
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Enhanced skills in recommendation system development using collaborative filtering techniques.
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Optimized database querying and indexing for fast property searches.
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Improved front-end development with Streamlit for interactive visualization.