187 lines
7.2 KiB
Plaintext
187 lines
7.2 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" CustomerID Gender Age Annual Income ($) Spending Score (1-100) \\\n",
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"0 1 Male 19 15000 39 \n",
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"1 2 Male 21 35000 81 \n",
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"2 3 Female 20 86000 6 \n",
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"3 4 Female 23 59000 77 \n",
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"4 5 Female 31 38000 40 \n",
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"5 6 Female 22 58000 76 \n",
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"6 7 Female 35 31000 6 \n",
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"7 8 Female 23 84000 94 \n",
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"8 9 Male 64 97000 3 \n",
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"9 10 Female 30 98000 72 \n",
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"\n",
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" Profession Work Experience Family Size \n",
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"0 Healthcare 1 4 \n",
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"1 Engineer 3 3 \n",
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"2 Engineer 1 1 \n",
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"3 Lawyer 0 2 \n",
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"4 Entertainment 2 6 \n",
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"5 Artist 0 2 \n",
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"6 Healthcare 1 3 \n",
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"7 Healthcare 1 3 \n",
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"8 Engineer 0 3 \n",
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"9 Artist 1 4 \n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"data = pd.read_csv(\"Customers.csv\")\n",
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"print(data[:10])"
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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": 61,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Gender Age Annual Income ($) Spending Score (1-100) Profession \\\n",
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"0 0.0 0.191919 0.078958 0.39 0.111111 \n",
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"1 0.0 0.212121 0.184236 0.81 0.222222 \n",
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"2 1.0 0.202020 0.452694 0.06 0.222222 \n",
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"3 1.0 0.232323 0.310569 0.77 0.333333 \n",
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"4 1.0 0.313131 0.200027 0.40 0.444444 \n",
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"5 1.0 0.222222 0.305305 0.76 0.555556 \n",
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"6 1.0 0.353535 0.163180 0.06 0.111111 \n",
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"7 1.0 0.232323 0.442166 0.94 0.111111 \n",
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"8 0.0 0.646465 0.510596 0.03 0.222222 \n",
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"9 1.0 0.303030 0.515860 0.72 0.555556 \n",
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"\n",
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" Work Experience Family Size \n",
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"0 0.058824 0.375 \n",
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"1 0.176471 0.250 \n",
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"2 0.058824 0.000 \n",
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"3 0.000000 0.125 \n",
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"4 0.117647 0.625 \n",
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"5 0.000000 0.125 \n",
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"6 0.058824 0.250 \n",
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"7 0.058824 0.250 \n",
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"8 0.000000 0.250 \n",
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"9 0.058824 0.375 \n"
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]
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}
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],
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"source": [
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"dataF = data\n",
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"\n",
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"# Changing words to numbers\n",
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"\n",
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"mapping = {'NaN' : 0, 'Healthcare' : 1, 'Engineer' : 2, 'Lawyer' : 3, 'Entertainment' : 4, 'Artist' : 5, 'Executive' : 6,\n",
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" 'Doctor' : 7, 'Homemaker' : 8, 'Marketing' : 9}\n",
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"\n",
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"mapping2 = {'Male' : 0, 'Female' : 1}\n",
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"\n",
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"dataF = dataF.replace({'Profession': mapping})\n",
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"dataF = dataF.replace({'Gender': mapping2})\n",
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"\n",
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"dataF = dataF.drop(columns=['CustomerID'])\n",
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"\n",
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"# Normalization\n",
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"\n",
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"dataF['Profession'] = dataF['Profession'].fillna(0)\n",
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"\n",
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"normalized_dataF = (dataF - dataF.min())/(dataF.max() - dataF.min())\n",
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"\n",
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"print(normalized_dataF[:10])"
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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": 62,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data = normalized_dataF[0:1600]\n",
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"dev_data = normalized_dataF[1600:1800]\n",
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"test_data = normalized_dataF[1800:]"
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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": 64,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wielkość zbioru Customers: 2000 elementów\n",
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"Wielkość zbioru trenującego: 1600 elementów\n",
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"Wielkość zbioru walidującego: 200 elementów\n",
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"Wielkość zbioru testującego: 200 elementów\n",
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" \n",
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"Dane i wartości na temat zbioru: \n",
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" \n",
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" Gender Age Annual Income ($) Spending Score (1-100) \\\n",
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"count 2000.000000 2000.000000 2000.000000 2000.000000 \n",
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"mean 0.593000 0.494545 0.582879 0.509625 \n",
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"std 0.491398 0.287169 0.240767 0.279347 \n",
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"min 0.000000 0.000000 0.000000 0.000000 \n",
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"25% 0.000000 0.252525 0.392538 0.280000 \n",
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"50% 1.000000 0.484848 0.579263 0.500000 \n",
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"75% 1.000000 0.737374 0.784806 0.750000 \n",
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"max 1.000000 1.000000 1.000000 1.000000 \n",
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"\n",
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" Profession Work Experience Family Size \n",
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"count 2000.000000 2000.000000 2000.000000 \n",
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"mean 0.467167 0.241324 0.346062 \n",
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"std 0.250289 0.230718 0.246344 \n",
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"min 0.000000 0.000000 0.000000 \n",
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"25% 0.222222 0.058824 0.125000 \n",
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"50% 0.555556 0.176471 0.375000 \n",
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"75% 0.555556 0.411765 0.500000 \n",
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"max 1.000000 1.000000 1.000000 \n"
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]
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}
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],
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"source": [
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"print(f\"Wielkość zbioru Customers: {len(data)} elementów\")\n",
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"print(f\"Wielkość zbioru trenującego: {len(train_data)} elementów\")\n",
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"print(f\"Wielkość zbioru walidującego: {len(dev_data)} elementów\")\n",
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"print(f\"Wielkość zbioru testującego: {len(test_data)} elementów\")\n",
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"\n",
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"print(f\" \\nDane i wartości na temat zbioru: \\n \\n {normalized_dataF.describe()}\")"
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]
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}
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],
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"metadata": {
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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": "3.10.6"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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