<jupyter_start><jupyter_text>House Price Predictions![title](https://cdn.pixabay.com/photo/2016/10/06/13/38/houses-1719055_960_720.png)How do you search for your house!? I bet, the 1st thing cross the mind is the price. Price plays a important role in buying the house. The features such as area, number of bedrooms , amenities all play major factors which determine the price of the houses. In this dataset,we will be looking into various features which determine the price and factors that determines it.The dataset is downloaded from kaggle at this link : https://www.kaggle.com/c/house-prices-advanced-regression-techniques .We will we be working on the data explorations, analysis and cleaning to predict the prices of the houses.<jupyter_code># Import the necessary datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

pd.set_option('display.max_columns',100)
train=pd.read_csv('/Users/anoopsp/Desktop/Data Science Portfolio Projects/datasets/home-data-for-ml-course/train.csv')
train.info
Null_values=[columns for columns in train.columns if train[columns].isna().sum()>0]
for col in Null_values:
    print(f'The percentage of missing value in {col}',np.round(train[col].isna().sum()/train.shape[0] *100,2),'%')<jupyter_output>The percentage of missing value in LotFrontage 17.74 %
The percentage of missing value in Alley 93.77 %
The percentage of missing value in MasVnrType 0.55 %
The percentage of missing value in MasVnrArea 0.55 %
The percentage of missing value in BsmtQual 2.53 %
The percentage of missing value in BsmtCond 2.53 %
The percentage of missing value in BsmtExposure 2.6 %
The percentage of missing value in BsmtFinType1 2.53 %
The percentage of missing value in BsmtFinType2 2.6 %
The percentage of missing value in Electrical 0.07 %
The percentage of missing value in FireplaceQu 47.26 %
The percentage of missing value in GarageType 5.55 %
The percentage of missing value in GarageYrBlt 5.55 %
The percentage of missing value in GarageFinish 5.55 %
The percentage of missing value in GarageQual 5.55 %
The percentage of missing value in GarageCond 5.55 %
The percentage of missing value in PoolQC 99.52 %
The percentage of missing value in Fence 80.75 %
The percentage of missing value in MiscFeature 96.[...]<jupyter_text>We notice these columns has missing values , we need to understand how these missing values has any effect on SalePrice values. We need to identify Numerical and Categorical columns for further analysis.<jupyter_code>Numerical_columns=train.select_dtypes(exclude='object').columns
train[Numerical_columns]
Categorical_columns=train.select_dtypes(include='object').columns
train[Categorical_columns].head()
(len(Numerical_columns)+len(Categorical_columns))==len(train.columns)<jupyter_output><empty_output><jupyter_text>We have divided our columns into numerical and categorical , we will explore these columns to do a exploratory data analysis. We noticed year sold column, will have a check how the trend of the sale price.<jupyter_code>train.groupby('YrSold')['SalePrice'].median().plot();<jupyter_output><empty_output>
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