HU Machine Learning Lab by Anaconda Principal Component Analysis Worksheet
Description
- For the lab program uploaded in the blackboard, analyze the dataset and generate reports indicating the changes in the value of accuracy when the n_components value ranges from 15 to 20 with different dimensionality reduction techniques along with different classifiers.
- by using this code
- { “cells”: [ { “cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “n”, “1-PCAn”, “2-FAn”, “3-LDAn”, “4-ISOn”, “5-LLEn”, “n”, “Enter your choice: 1n”, “n”, “1-NBn”, “2-KNNn”, “3-LRn”, “4-DTn”, “5-RFn”, “n”, “Enter your choice: 3n”, “0.9789160401002507n” ] } ], “source”: [ “import pandas as pdn”, “import numpy as npn”, “from pandas import read_csvn”, “n”, “#from sklearn.feature_selection import SelectKBestn”, “#from sklearn.feature_selection import f_classifn”, “from sklearn.decomposition import PCAn”, “from sklearn.decomposition import FactorAnalysisn”, “from sklearn.discriminant_analysis import LinearDiscriminantAnalysisn”, “from sklearn.manifold import Isomapn”, “from sklearn.manifold import LocallyLinearEmbeddingn”, “n”, “n”, “from sklearn import model_selectionn”, “from sklearn.linear_model import LogisticRegressionn”, “import mathn”, “from sklearn.neighbors import KNeighborsClassifiern”, “from sklearn.preprocessing import StandardScalern”, “from sklearn.naive_bayes import GaussianNBn”, “from sklearn.tree import DecisionTreeClassifiern”, “from sklearn.svm import SVCn”, “from sklearn.ensemble import RandomForestClassifiern”, “#from sklearn.ensemble import AdaBoostClassifiern”, “#from sklearn.ensemble import GradientBoostingClassifiern”, “n”, “#filename = ‘pima-indians-diabetes.data.csv’n”, “filename = ‘wdbc.csv’n”, “n”, “dataframe = read_csv(filename)n”, “array = dataframe.valuesn”, “n”, “n”, “X1 = array[:,:-1]n”, “Y1 = array[:,-1]n”, “scaler = StandardScaler().fit(X1)n”, “rescaledX = scaler.transform(X1)n”, “X1= rescaledXn”, “n”, “def dr_pca():n”, ” global X1n”, ” pca = PCA(n_components=18)n”, ” X1=pca.fit_transform(X1)n”, “n”, “def dr_fa():n”, ” global X1n”, ” fa = FactorAnalysis(n_components=18, random_state=0)n”, ” X1 = fa.fit_transform(X1)n”, ” n”, “def dr_lda():n”, ” global X1n”, ” #lda = LinearDiscriminantAnalysis(n_components=18)n”, ” #ValueError: n_components cannot be larger than min(n_features, n_classes – 1).n”, ” #CORRECT ONE BELOWn”, ” #lda = LinearDiscriminantAnalysis(n_components=1) n”, ” lda = LinearDiscriminantAnalysis()n”, ” X1=lda.fit_transform(X1,Y1)n”, ” n”, “def dr_iso():n”, ” global X1n”, ” iso = Isomap(n_components=10)n”, ” X1 = iso.fit_transform(X1)n”, ” n”, “def dr_lle():n”, ” global X1n”, ” lle = LocallyLinearEmbedding(n_components=18)n”, ” X1 = lle.fit_transform(X1)n”, ” n”, “n”, “print(“””””n””
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