House-Price-Prediction-Advanced-Regression

Imported all the required library

import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

Loading and Viewing the data

data=pd.read_csv('housing.csv')
data.head()

Data Visualisation

Ploting the Heatmap

Ploting Ocean Proximity

Ploting Lattitude effect on house price

Ploting Ocean Median Price

Ploting Income effect on house price

Data filling

housing_mean=housing_data.fillna(housing_data['total_bedrooms'].mean())

Using Different Model's

Creating Training and Testing Data set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)

Training the model

model=LogisticRegression()
model.fit(x_train,y_train)

Making the prediction

new_prediction=model.predict(testing_data)

Getting the accuracy score

from sklearn.metrics import mean_squared_error
rmse = np.sqrt(mean_squared_error(prediction, y_test))
rmse

Got RMSE value of 69140.009