Housing-Price-Prediction

Imported all the required library

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import cross_validation
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor

Loading and Viewing the data

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

Data Visualisation

Ploting the Heatmap

Ploting Histogram of Saleprice

Ploting effect of 2nd floor on house price

Ploting effect of Lot Area on Sale Price

Ploting Basement area on house price

Ploting effect of MasVnr Area on Sale Price

Ploting effect of Garage Year on Sale Price

Ploting effect of Year Renowed on Sale Price

Ploting effect of 1st floor on Sale Price

Ploting effect of Year Build on Sale Price

Ploting effect of Basement area on Sale Price

Ploting effect of Grive area on Sale Price

Ploting effect of Garage area on Sale Price

Ploting effect of Cars on Sale Price

Ploting effect of Total rooms on Sale Price

Ploting effect of Overall quality on Sale Price

Ploting effect of Fireplaces on Sale Price

Converting Categorical data into Continous using Label Encoder

df.GarageCond=le.fit_transform(df.GarageCond)
df.GarageYrBlt=le.fit_transform(df.GarageYrBlt)
df.GarageFinish=le.fit_transform(df.GarageFinish)

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

Here only shown one algorithm in the notebook all other algorithm are used there.