{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finance & Accounting Courses in udemy.com\n", "## Includes:\n", "* id\n", "* title\n", "* is_paid\n", "* num_subscribers\n", "* rating\n", "* num_reviews\n", "* created" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import preprocessing\n", "import kaggle\n", "\n", "kaggle.api.authenticate()\n", "kaggle.api.dataset_download_files('jilkothari/finance-accounting-courses-udemy-13k-course', path='.', unzip=True)\n", "\n", "courses = pd.read_csv('courses.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtitleurlis_paidnum_subscribersratingnum_reviewscreated
0762616the_complete_sql_bootcamp_2020:_go_from_zero_t.../course/the-complete-sql-bootcamp/True2955094.7780062016-02-14T22:57:48Z
1937678tableau_2020_a-z:_hands-on_tableau_training_fo.../course/tableau10/True2090704.6545812016-08-22T12:10:18Z
21361790pmp_exam_prep_seminar_-__pmbok_guide_6/course/pmp-pmbok6-35-pdus/True1552824.6526532017-09-26T16:32:48Z
3648826the_complete_financial_analyst_course_2020/course/the-complete-financial-analyst-course/True2458604.5464472015-10-23T13:34:35Z
4637930an_entire_mba_in_1_course:award_winning_busine.../course/an-entire-mba-in-1-courseaward-winning...True3748364.5416302015-10-12T06:39:46Z
...........................
135313171702máster_en_inversión_bursátil,_completo_análisi.../course/master-en-inversion-bursatil-completo-...False4854.4112020-05-26T17:34:49Z
135322925096curso_do_zero_a_investidor_em_ações_na_bolsa/course/curso-do-zero-a-investidor-em-acoes-na...False2604.2112020-03-28T18:39:36Z
135333146788day_trading_kumo-méthode_de_trading_range-_for.../course/day-trading-kumo-methode-de-trading-ra...False1214.1102020-05-19T17:08:48Z
135342400574investindo_do_zero_com_tesouro_direto/course/investindo-do-zero-com-tesouro-direto-...False2333.6102019-06-05T23:08:57Z
135352888390acabou_a_previdência_e_agora?_-_volume_01/course/acabou-a-previdencia-e-agora-volume-01/False1754.5102020-03-20T01:41:25Z
\n", "

9501 rows × 8 columns

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" ], "text/plain": [ " id title \\\n", "0 762616 the_complete_sql_bootcamp_2020:_go_from_zero_t... \n", "1 937678 tableau_2020_a-z:_hands-on_tableau_training_fo... \n", "2 1361790 pmp_exam_prep_seminar_-__pmbok_guide_6 \n", "3 648826 the_complete_financial_analyst_course_2020 \n", "4 637930 an_entire_mba_in_1_course:award_winning_busine... \n", "... ... ... \n", "13531 3171702 máster_en_inversión_bursátil,_completo_análisi... \n", "13532 2925096 curso_do_zero_a_investidor_em_ações_na_bolsa \n", "13533 3146788 day_trading_kumo-méthode_de_trading_range-_for... \n", "13534 2400574 investindo_do_zero_com_tesouro_direto \n", "13535 2888390 acabou_a_previdência_e_agora?_-_volume_01 \n", "\n", " url is_paid \\\n", "0 /course/the-complete-sql-bootcamp/ True \n", "1 /course/tableau10/ True \n", "2 /course/pmp-pmbok6-35-pdus/ True \n", "3 /course/the-complete-financial-analyst-course/ True \n", "4 /course/an-entire-mba-in-1-courseaward-winning... True \n", "... ... ... \n", "13531 /course/master-en-inversion-bursatil-completo-... False \n", "13532 /course/curso-do-zero-a-investidor-em-acoes-na... False \n", "13533 /course/day-trading-kumo-methode-de-trading-ra... False \n", "13534 /course/investindo-do-zero-com-tesouro-direto-... False \n", "13535 /course/acabou-a-previdencia-e-agora-volume-01/ False \n", "\n", " num_subscribers rating num_reviews created \n", "0 295509 4.7 78006 2016-02-14T22:57:48Z \n", "1 209070 4.6 54581 2016-08-22T12:10:18Z \n", "2 155282 4.6 52653 2017-09-26T16:32:48Z \n", "3 245860 4.5 46447 2015-10-23T13:34:35Z \n", "4 374836 4.5 41630 2015-10-12T06:39:46Z \n", "... ... ... ... ... \n", "13531 485 4.4 11 2020-05-26T17:34:49Z \n", "13532 260 4.2 11 2020-03-28T18:39:36Z \n", "13533 121 4.1 10 2020-05-19T17:08:48Z \n", "13534 233 3.6 10 2019-06-05T23:08:57Z \n", "13535 175 4.5 10 2020-03-20T01:41:25Z \n", "\n", "[9501 rows x 8 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "courses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delete redundant columns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "imp_col = ['id', 'title', 'url', 'is_paid', 'num_subscribers', 'rating', 'num_reviews', 'created']\n", "courses = courses[imp_col]\n", "courses.to_csv(\"courses.csv\", index=False)\n", "courses = pd.read_csv('courses.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delete empty rows of rating column and number of reviews less than 10" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "rating_col = 'rating'\n", "num_reviews_col = 'num_reviews'\n", "courses = courses.drop(courses[courses.rating == 0].index)\n", "courses = courses.drop(courses[courses.num_reviews < 10].index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simplify numbers to one decimal place and format 'title' column to specifc schema" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "courses = courses.round(1)\n", "courses['title'] = courses['title'].str.lower()\n", "courses['title'] = courses['title'].str.replace(\" \", \"_\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delete artifacts" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "courses = courses.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Split dataset into 60% 20% 20% - train, valid, test" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "courses_train, courses_validate, courses_test = np.split(courses.sample(frac=1), [int(.6*len(courses)), int(.8*len(courses))])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary of train, valid, test" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Courses: 76008\n", "Courses (train) : 45600\n", "Courses (validate): 15200\n", "Courses (test) 15208\n" ] } ], "source": [ "print(\"Courses: \".ljust(20), np.size(courses))\n", "print(\"Courses (train) : \".ljust(20), np.size(courses_train))\n", "print(\"Courses (validate): \".ljust(20), np.size(courses_validate))\n", "print(\"Courses (test) \".ljust(20), np.size(courses_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Describe numeric columns" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnum_subscribersratingnum_reviews
count9501.09501.09501.09501.0
mean1484700.33953.94.1346.6
std887299.711103.90.41882.7
min2762.013.01.510.0
25%718252.0261.03.921.0
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" ], "text/plain": [ " id num_subscribers rating num_reviews\n", "count 9501.0 9501.0 9501.0 9501.0\n", "mean 1484700.3 3953.9 4.1 346.6\n", "std 887299.7 11103.9 0.4 1882.7\n", "min 2762.0 13.0 1.5 10.0\n", "25% 718252.0 261.0 3.9 21.0\n", "50% 1413712.0 1170.0 4.2 49.0\n", "75% 2193058.0 3644.0 4.4 157.0\n", "max 3477486.0 374836.0 5.0 78006.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "courses.describe().round(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Distribution of 'is_paid' column" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pvpbkHOAfgXuSHAKOTHlOM8336c+gJOdX1X9Mex7S8SR5Y1XtT/I64JsMlpp/Bvg+4J6qen6qE5xhRl/SxCV5rKoub9ufqKqfmvaceuELuZKmYXgJ8s1Tm0WHjL6kaajjbOs0c3lH0sQleYnBp8QDLAO+8fJd+DUMp5XRl6SOuLwjSR0x+pLUEaMvSR0x+pLUEaMvSR35P2rtiiPqFJ4dAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.value_counts(courses['is_paid']).plot(kind=\"bar\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Current dataset" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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