House Price Prediction Project proves to be the Hello World of the Machine Learning world. It is a very easy project which simply uses Linear Regression to predict house prices. This is going to be a very short blog, so without any further due.
Let’s do it…
Step 1 – Importing required libraries.
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score %matplotlib inline
Step 2 – Reading our input data for House Price Prediction.
customers = pd.read_csv('USA_Housing.csv') customers.head()
Step 3 – Describing our data.
Step 4 – Analyzing information from our data.
Step 5 – Plots to visualize data of House Price Prediction.
- We use sns.pairplot(data) to plot all the possible combinations of numerical columns in the dataset.
- From the plots below we can infer one thing that Price is highly correlated to Average Area Income.
Step 6 – Scaling our data.
scaler = StandardScaler() X=customers.drop(['Price','Address'],axis=1) y=customers['Price'] cols = X.columns X = scaler.fit_transform(X)
- We need to scale our data to bring everything down to one scale or within one range.
- We are using StandardScaler here to scale our data.
- Just check out the 1st image of input data and see how different columns belong to different scales.
Step 7 – Splitting our data for train and test purposes.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
- Using train_test_split() to split our data in 70%-30% proportions.
Step 8 – Training our Linear Regression model for House Price Prediction.
lr = LinearRegression() lr.fit(X_train,y_train) pred = lr.predict(X_test) r2_score(y_test,pred)
- We are using r2_score here to measure the performance of our regression model.
- Our model is giving a 0.91 r2_score out of 1 which is a very decent score.
- I also tried using Lasso and Ridge Regressions but they also performed nearly the same as Linear regression.
Step 9 – Lets visualize our predictions of House Price Prediction.
- This should be a straight line for a 100% accurate model.
- But we are also getting a trend like a straight line which is also not bad.
Step 10 – Plotting the residuals of our House Price Prediction model.
- Here we are plotting a histogram of residuals.
- Residual is the error term in a regression, or we can say the difference between real value and our predicted value.
- As we can see that most of the residuals are around 0 means our predictions are almost near to the real values, hence it is a very good model.
Step 11 – Observing the coefficients.
cdf=pd.DataFrame(lr.coef_, cols, ['coefficients']).sort_values('coefficients',ascending=False) cdf
- These are the coefficients calculated while Linear Regression.
- Its intuition is that a 1 unit increase in Avg. Area Income will lead to an increase of $230377.522 in the price of the house, assuming all other factors are kept constant.
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So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time ?…