House-price-Prediction
Import Libraries
%matplotlib inline import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.manifold import MDS
Random Sampling
Random sampling approach (i.e train_test_split), using a test size of 30% of data and a random_state of 42.
# X--> feature set ,, y --> target variable x = df1.drop(['id', 'price'],axis=1) y = df1['price'] x_train , x_test , y_train , y_test = train_test_split(x , y , test_size = 0.30,random_state =42) print('shapes of training and test set ') x_train.shape,x_test.shape
Straitified Sampling
target = 'price' X = df1.drop(target, axis = 'columns', inplace = False) Y = df1[target]
#method: 2 df2 = df1[df1[target].isin(df1[target].value_counts()[df1[target].value_counts()>2].index)] y2 = df2[target] X2 = df2.fillna(0)
X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.33, random_state=42, stratify=X2[target])
X2_train.shape,X2_test.shape
K- mean(elbow)
The Elbow method is a very popular technique and the idea is to run k-means clustering for a range of clusters k (let’s say from 1 to 10) and for each value, we are calculating the sum of squared distances from each point to its assigned center(distortions).
from matplotlib import style from sklearn.cluster import KMeans
df1 = df1.drop('date', axis = 'columns', inplace = False)
distortions = [] K = range(1,11) for k in K: kmeanModel = KMeans(n_clusters=k) kmeanModel.fit(df1) distortions.append(kmeanModel.inertia_)
plt.figure(figsize=(8,2)) plt.plot(K, distortions, 'bx-') plt.xlabel('k') plt.ylabel('Distortion') plt.title('The Elbow Method showing the optimal k') plt.show()
Dimension reduction on both org and 2 types of reduced data using PCA
#import libraries
from sklearn.decomposition import PCA model = PCA()
#fit into model
model.fit(df1)
#transform model
transformed = model.transform(df1) print('Principle components: ',model.components_)
# PCA variance from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df1 = scaler.fit_transform(df1) pca = PCA() pca.fit_transform(df1) pca_variance = pca.explained_variance_ plt.bar(range(pca.n_components_), pca_variance) plt.xlabel('PCA feature') plt.ylabel('variance') plt.show()
Intrinsic dimension
PCA identifies intrinsic dimension when samples have any number of features
intrinsic dimension = number of PCA feature with significant variance
In order to choose intrinsic dimension try all of them and find best accuracy
#color_list=['black','gray'] pca = PCA(n_components = 3) pca.fit(df1) transformed = pca.transform(df1) transformed.shape
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