diff --git a/EksplorasiData_Jeremy Aditya/takeMeOut.ipynb b/EksplorasiData_Jeremy Aditya/takeMeOut.ipynb new file mode 100644 index 0000000..512ee18 --- /dev/null +++ b/EksplorasiData_Jeremy Aditya/takeMeOut.ipynb @@ -0,0 +1,1780 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "takeMeOut.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "p3MLjSENLg__" + }, + "source": [ + "Tugas Developer Student Clubs\n", + "\n", + "* Nama : Jeremy Aditya\n", + "* NIM : 16520437\n", + "* Universitas : Institut Teknologi Bandung\n", + "* Mulai Pengerjaan : 14-11-2020\n", + "* Sumber : Take Me Out\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CBeRQVwSpN7d" + }, + "source": [ + "import pandas as pd\n", + "from sklearn.neighbors import NearestNeighbors\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n" + ], + "execution_count": 110, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zjhML6jOpX93" + }, + "source": [ + "#membaca data dari file.csv\n", + "df = pd.read_csv('/content/takemeout.csv')" + ], + "execution_count": 111, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MNATtkz6phT0", + "outputId": "e6296bd4-5f2c-44d2-fe54-738c6801c757", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 788 + } + }, + "source": [ + "df" + ], + "execution_count": 112, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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TimestampSiapa nama kamu?Cewek atau cowok nih?Seberapa penting quality time bareng calon pacar untuk kamu?Seberapa penting physical touch sama calon pacar untuk kamu?Seberapa penting word of affirmation dari calon pacar untuk kamu?Seberapa penting dapet kado dari calon pacar untuk kamu?Seberapa penting bantuan dari calon pacar untuk kamu?
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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "73Laa0oXPtDH" + }, + "source": [ + "Analisa\n", + "\n", + "Dari tabel diatas, dapat kita simpulkan bahwa dari 101 data, 81% adalah laki-laki dan 19% adalah perempuan. Lalu, secara rata-rata, pria lebih mementingkan quality time, physical touch, dan affirmative words daripada wanita. Secara garis besar, hasil yang di dapatkan dari wanita dan pria hampir sama. Perbedaan yang cukup mencolok terdapat di physical touch. Mayoritas wanita menaruh physical touch di point 1 yaitu tidak lah penting. Hal ini cukup berbeda dengan pria dimana mayoritas menganggap physical touch di point 3 yaitu biasa saja yang berarti secara umum, pria lebih menganggap physical touch sebagai hal yang penting dari pada wanita." + ] + } + ] +} \ No newline at end of file diff --git a/takemeout.csv b/EksplorasiData_Jeremy Aditya/takemeout.csv similarity index 98% rename from takemeout.csv rename to EksplorasiData_Jeremy Aditya/takemeout.csv index cf476a7..df46942 100644 --- a/takemeout.csv +++ b/EksplorasiData_Jeremy Aditya/takemeout.csv @@ -1,102 +1,102 @@ -Timestamp,Siapa nama kamu?,Cewek atau cowok nih?,Seberapa penting quality time bareng calon pacar untuk kamu?,Seberapa penting physical touch sama calon pacar untuk kamu?,Seberapa penting word of affirmation dari calon pacar untuk kamu?,Seberapa penting dapet kado dari calon pacar untuk kamu?,Seberapa penting bantuan dari calon pacar untuk kamu? -2020/10/31 3:39:25 PM GMT+7,A**************,Cowok,5,5,4,1,3 -2020/10/31 3:39:36 PM GMT+7,L****,Cewek,5,5,3,2,2 -2020/10/31 3:39:38 PM GMT+7,Y***********,Cowok,4,3,4,4,4 -2020/10/31 3:39:42 PM GMT+7,a***,Cowok,5,5,5,2,3 -2020/10/31 3:39:43 PM GMT+7,B****,Cowok,5,5,5,2,4 -2020/10/31 3:39:44 PM GMT+7,M*********,Cowok,5,5,4,2,2 -2020/10/31 3:39:45 PM GMT+7,F***********************************,Cowok,4,2,5,3,4 -2020/10/31 3:39:47 PM GMT+7,F***,Cowok,3,3,4,2,4 -2020/10/31 3:39:50 PM GMT+7,R******************,Cowok,5,2,4,1,4 -2020/10/31 3:40:05 PM GMT+7,s******,Cowok,4,1,1,5,5 -2020/10/31 3:40:07 PM GMT+7,A,Cowok,5,4,5,2,3 -2020/10/31 3:40:08 PM GMT+7,R**,Cowok,3,4,3,3,5 -2020/10/31 3:40:12 PM GMT+7,D***,Cowok,5,4,3,3,4 -2020/10/31 3:40:14 PM GMT+7,F***,Cowok,1,3,5,1,5 -2020/10/31 3:40:20 PM GMT+7,P****,Cowok,4,3,4,5,5 -2020/10/31 3:40:21 PM GMT+7,A*,Cowok,4,3,5,4,4 -2020/10/31 3:40:22 PM GMT+7,M***********************,Cowok,1,1,1,1,1 -2020/10/31 3:40:25 PM GMT+7,J****************,Cowok,5,4,5,3,5 -2020/10/31 3:40:27 PM GMT+7,J***,Cowok,5,3,4,3,4 -2020/10/31 3:40:28 PM GMT+7,M**************,Cewek,5,5,5,5,5 -2020/10/31 3:40:31 PM GMT+7,F*************,Cewek,4,2,3,1,3 -2020/10/31 3:40:31 PM GMT+7,b***********,Cowok,5,4,3,2,3 -2020/10/31 3:40:31 PM GMT+7,u***,Cowok,5,3,3,5,5 -2020/10/31 3:40:34 PM GMT+7,B***,Cewek,5,2,4,5,4 -2020/10/31 3:40:35 PM GMT+7,h***,Cowok,5,4,4,4,3 -2020/10/31 3:40:36 PM GMT+7,E***,Cewek,3,2,3,1,5 -2020/10/31 3:40:36 PM GMT+7,q*,Cowok,5,5,5,5,5 -2020/10/31 3:40:37 PM GMT+7,v****,Cewek,3,3,3,3,3 -2020/10/31 3:40:40 PM GMT+7,R****,Cowok,5,3,3,2,3 -2020/10/31 3:40:41 PM GMT+7,D****,Cowok,4,5,4,2,4 -2020/10/31 3:40:42 PM GMT+7,A*************,Cowok,3,3,3,3,3 -2020/10/31 3:40:42 PM GMT+7,B**************,Cowok,3,2,4,1,5 -2020/10/31 3:40:42 PM GMT+7,S************,Cowok,2,2,2,1,1 -2020/10/31 3:40:43 PM GMT+7,H***,Cewek,4,2,2,2,5 -2020/10/31 3:40:43 PM GMT+7,J*****,Cowok,5,3,4,3,4 -2020/10/31 3:40:44 PM GMT+7,R****,Cowok,5,3,5,5,5 -2020/10/31 3:40:45 PM GMT+7,a******,Cowok,3,1,3,1,2 -2020/10/31 3:40:45 PM 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-Spesifikasi tugas: -1. Program memaparkan analisis data berupa tabel atau grafik, dan penjelasan lisan dari dataset terkait. -2. Dataset yang akan digunakan untuk analisis dibebaskan. Jika menggunakan dataset dari sumber lain, cantumkan juga sumber dataset tersebut. -3. Dataset harus dibersihkan terlebih dahulu sebelum melakukan analisis. Beri penjelasan lisan mengenai analisis yang dilakukan menggunakan markdown. -4. Notebook tersebut harus memuat: - a. Nama lengkap, NIM, dan asal universitas - b. Tanggal mulai pengerjaan tugas - c. Sumber dataset (jika menggunakan dataset TakeMeOut, maka tulis saja TakeMeOut) - d. Tahapan analisis data misalnya "Data Observation", kemudian "Data Cleaning", dst - e. Statistik dasar data (min, max, avg, dst) dan analisis dasar dari Youtube https://youtu.be/8J1tGYEvf68 - f. Insight baru mengenai data tersebut berdasarkan eksplorasi sendiri (tidak ada format, dibebaskan) minimal 2. Ini misalnya "A memiliki korelasi positif dengan B", dst -5. Penggunaan library dibebaskan. - -Prosedur pengerjaan: -1. Tugas dikerjakan secara individu -2. Tugas ini dikumpulkan paling lambat hari sabtu 15 November 2020 pukul 23.59 WIB dengan mengajukan pull request pada repo github DSC https://github.com/gdscitb/DSCDataScience -3. File submisi berisikan 1 notebook .ipynb dan 1 file dataset .csv yang terpakai -3. Dilarang keras menyalin program dari sumber lain (buku, internet, program kakak tingkat, program kelompok lain) \ No newline at end of file