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# Random Sampling, Straitified Sampling, K- mean(elbow), MDS In Machine Learning | ML Assignment Help

## 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 (letâ€™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)

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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