199 lines
3.7 KiB
Plaintext
199 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EX6m-JG4-1E_"
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},
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"source": [
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"To standardize a feature means to shift all of its values so that their mean = 0 and to scale them so that their variance = 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "p3Z6ftQr_V51"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"data = pd.read_csv(\"file_pe_headers.csv\", sep =\",\")\n",
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"X = data.drop([\"Name\", \"Malware\"], axis=1).to_numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(X.shape)\n",
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"print(X)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### standarize data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Next standarize x using StandardScaler instance\n",
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"from sklearn.preprocessing import StandardScaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"### standarize 1-dimension data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get e_lfanew\n",
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"data1st = X[:,16].reshape(-1, 1)\n",
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"print(data1st.shape)\n",
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"print(data1st)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data1st_standardized = StandardScaler().fit_transform(data1st)\n",
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"\n",
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"print(data1st_standardized.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(data1st_standardized)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### standarize multi-dimension data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "N1fKkOaj_2Qe"
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},
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"outputs": [],
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"source": [
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"X_standardized = StandardScaler().fit_transform(X)\n",
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"\n",
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"print(X_standardized.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "dE5vcO6kAJ4f",
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"outputId": "9b5eea03-73dd-4b30-e266-e275e95ca32f"
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},
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"outputs": [],
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"source": [
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"print(X_standardized)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "2eB22gEeAkRt"
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},
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"source": [
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"It is important to standardize features because performance is highly sensitive to the relative scale of features."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"name": "Standardizing data Udemy.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"widgets": {
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"application/vnd.jupyter.widget-state+json": {
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"state": {},
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"version_major": 2,
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"version_minor": 0
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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