🏠 House Price Prediction

This is an interactive web application that predicts house prices based on user inputs like area, number of bedrooms, bathrooms, and other house features. It uses a Random Forest Regressor model trained on a housing dataset and is deployed using Streamlit for an interactive user interface. The app also provides a feature importance visualization to help users understand which features affect the price prediction.

Table of Contents:

1. Features
2. Requirements
3. Setup Instructions
4. Folder Structure
5. Model Training

Features:

• Price Prediction: Users can input various house features, such as area, number of bedrooms, and bathrooms, and the app will predict the house price.
• Feature Importance: The model shows a bar chart indicating the importance of each feature in predicting the price.
• Dataset Exploration: Users can explore the dataset used for training, including a preview and summary statistics.
• Interactive Interface: Easy-to-use sidebar for entering house features and receiving predictions.

Requirements:

Make sure you have Python 3.7 or higher installed, along with the following libraries:

• pandas
• numpy
• scikit-learn
• streamlit
• matplotlib
• pickle

To install the required dependencies, use the requirements.txt file provided in the project.

Setup Instructions:

1. Clone the repository:

git clone https://github.com/Vidish-Bijalwan/house-price-prediction.git
cd house-price-prediction

### 2. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'

### 3. Install the required dependencies:

pip install -r requirements.txt

### 4.  Run the Streamlit app:
streamlit run app.py


##Folder Structure:
The project structure is as follows:

house-price-prediction/
│
├── data/
│   └── Housing.csv         # Housing dataset for training the model
├── model/
│   └── model.pkl           # Trained model and scaler
├── app.py                  # Streamlit app to make predictions
├── requirements.txt        # List of dependencies
└── README.md               # This file


## Model Training:

The model is trained using Random Forest Regressor. Here’s an overview of the steps followed to train the model:

Load and Preprocess Data: The dataset (data/Housing.csv) is preprocessed by encoding categorical features and scaling numerical features.
Train Model: A Random Forest Regressor is trained on the preprocessed data.
Save Model: The trained model and the scaler are saved as model/model.pkl, which is then loaded in the Streamlit app for predictions.

# Model training code example
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pickle

# Load dataset
housing_data = pd.read_csv('data/Housing.csv')

# Preprocess dataset
categorical_columns = ['mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'prefarea', 'furnishingstatus']
housing_data_encoded = pd.get_dummies(housing_data, columns=categorical_columns, drop_first=True)

scaler = MinMaxScaler()
numerical_columns = ['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'parking']
housing_data_encoded[numerical_columns] = scaler.fit_transform(housing_data_encoded[numerical_columns])

# Split data
X = housing_data_encoded.drop('price', axis=1)
y = housing_data_encoded['price']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor(random_state=42, n_estimators=100)
model.fit(X_train, y_train)

# Save model
with open('model/model.pkl', 'wb') as file:
    pickle.dump((model, scaler), file)