Clustering-Challenge

SPRING CAMP RECRUITMENT TASK import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsRegressor from sklearn.cluster import KMeans import matplotlib.pyplot as plt import gradio as gr

Load the dataset

url = "link_to_kaggle_dataset" df = pd.read_csv(url)

Explore the dataset

print(df.head()) print(df.info())

Handle missing values, categorical variables, and feature scaling if needed

For simplicity, we assume the dataset is clean and numerical

Separate features and target variable

X = df.drop('SalePrice', axis=1) y = df['SalePrice']

Split the dataset into training and testing sets

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

Initialize KNN model and fit on the training data

knn_model = KNeighborsRegressor(n_neighbors=5) knn_model.fit(X_train, y_train)

Evaluate the model

accuracy = knn_model.score(X_test, y_test) print(f"KNN Model Accuracy: {accuracy}")

Use k-means clustering to gain insights into factors associated with houses of similar prices

kmeans = KMeans(n_clusters=3, random_state=42) df['cluster'] = kmeans.fit_predict(X)

Visualize clustering

plt.scatter(df['Feature1'], df['Feature2'], c=df['cluster'], cmap='viridis') plt.title('Clustering of Houses based on Features') plt.xlabel('Feature1') plt.ylabel('Feature2') plt.show()

Define the Gradio interface for house price prediction

def predict_price(features): features = np.array(features).reshape(1, -1) prediction = knn_model.predict(features)[0] return f'Predicted House Price: ${prediction:.2f}'

iface = gr.Interface(fn=predict_price, inputs="text", outputs="text") iface.launch(share=True)