{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Analysis of Medium Stats\n", "\n", "In this notebook, we will analyze my Medium article stats. The functions for scraping and formatting the data were developed in the `Development` notebook, and here we will focus on looking at the data quantitatively and visually.\n", "\n", "## Instructions\n", "\n", "To apply to your own medium data\n", "\n", "1. Go to the stats page https://medium.com/me/stats\n", "2. Make sure to scroll all the way down to the bottom so all the articles are loaded\n", "3. Right click, and hit 'save as'. \n", "4. Save the file as `stats.html` in the `data/` directory. You can also save the responses to do a similar analysis.\n", "\n", "![](images/stats-saving-medium.gif)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-12-30T19:26:29.786478Z", "start_time": "2018-12-30T19:26:29.660649Z" } }, "source": [ " # Might need to run this on MAC for multiprocessing to work properly\n", " # see https://stackoverflow.com/questions/50168647/multiprocessing-causes-python-to-crash-and-gives-an-error-may-have-been-in-progr\n", " export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For any of the figures, I recommend opening them in plotly and touching them up. `plotly` is an incredible library and I highly it as a replacement for whatever plotting library you are using." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Retrieve Statistics\n", "\n", "Thanks to a few functions already developed, you can get all of the statistics for your articles in under 10 seconds." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:48:57.364766Z", "start_time": "2018-12-31T23:48:56.519122Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 121 entries in table.\n" ] } ], "source": [ "from retrieval import process_in_parallel, get_table_rows\n", "\n", "table_rows = get_table_rows(fname='stats.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of these entries is a separate article. To get the information about each article, we use the next function. This scrapes both the article metadata and the article itself (using `requests` and `BeautifulSoup`)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:04.958470Z", "start_time": "2018-12-31T23:48:58.444877Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 121 articles in 6.28 seconds.\n" ] }, { "data": { "text/html": [ "
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clapsdays_since_publicationfansnum_responsespublicationpublished_dateread_ratioread_timereadsstarted_date...typeviewsword_countclaps_per_wordediting_days<tag>Education<tag>Data Science<tag>Towards Data Science<tag>Machine Learning<tag>Python
1162569.14196320None2017-06-10 14:25:0041.617672017-06-10 14:24:00...published16118590.001076000000
11418561.82400830None2017-06-17 22:02:0033.1214522017-06-17 22:02:00...published15738910.004626000000
11750549.204130190None2017-06-30 12:55:0020.29422132017-06-30 12:00:00...published1050120250.004158000011
1110548.36152700None2017-07-01 09:08:0036.549192017-06-30 18:21:00...published5225330.000000000000
1090544.37387600None2017-07-05 08:51:008.931452017-07-03 20:18:00...published5638920.000000100000
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" ], "text/plain": [ " claps days_since_publication fans num_responses publication \\\n", "116 2 569.141963 2 0 None \n", "114 18 561.824008 3 0 None \n", "117 50 549.204130 19 0 None \n", "111 0 548.361527 0 0 None \n", "109 0 544.373876 0 0 None \n", "\n", " published_date read_ratio read_time reads started_date \\\n", "116 2017-06-10 14:25:00 41.61 7 67 2017-06-10 14:24:00 \n", "114 2017-06-17 22:02:00 33.12 14 52 2017-06-17 22:02:00 \n", "117 2017-06-30 12:55:00 20.29 42 213 2017-06-30 12:00:00 \n", "111 2017-07-01 09:08:00 36.54 9 19 2017-06-30 18:21:00 \n", "109 2017-07-05 08:51:00 8.93 14 5 2017-07-03 20:18:00 \n", "\n", " ... type views word_count claps_per_word editing_days \\\n", "116 ... published 161 1859 0.001076 0 \n", "114 ... published 157 3891 0.004626 0 \n", "117 ... published 1050 12025 0.004158 0 \n", "111 ... published 52 2533 0.000000 0 \n", "109 ... published 56 3892 0.000000 1 \n", "\n", " Education Data Science Towards Data Science \\\n", "116 0 0 0 \n", "114 0 0 0 \n", "117 0 0 0 \n", "111 0 0 0 \n", "109 0 0 0 \n", "\n", " Machine Learning Python \n", "116 0 0 \n", "114 0 0 \n", "117 1 1 \n", "111 0 0 \n", "109 0 0 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = process_in_parallel(table_rows=table_rows, processes=25)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis\n", "\n", "With the comprehensive data, we can do any sort of analysis we want. There's a lot of data here and I'm sure you'll be able to find other interesting things to do with the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:06.793852Z", "start_time": "2018-12-31T23:49:04.961605Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Data science imports\n", "import pandas as pd\n", "import numpy as np\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "# Options for pandas\n", "pd.options.display.max_columns = 25\n", "\n", "# Display all cell outputs\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = 'all'\n", "\n", "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "import plotly.figure_factory as ff\n", "from plotly.offline import iplot\n", "\n", "import cufflinks\n", "cufflinks.go_offline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlations\n", "\n", "We can start off by looking at correlations. We'll limit this to the `published` articles for now." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:06.880359Z", "start_time": "2018-12-31T23:49:06.796476Z" } }, "outputs": [ { "data": { "text/html": [ "
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clapsdays_since_publicationfansnum_responsesread_ratioread_timereadstitle_word_countviewsword_countclaps_per_wordediting_days<tag>Education<tag>Data Science<tag>Towards Data Science<tag>Machine Learning<tag>Python
claps1.00-0.130.990.89-0.05-0.110.750.090.73-0.120.76-0.010.270.360.530.180.26
days_since_publication-0.131.00-0.14-0.080.030.360.05-0.310.040.33-0.08-0.11-0.76-0.43-0.39-0.110.04
fans0.99-0.141.000.87-0.07-0.110.760.100.75-0.120.73-0.000.270.370.540.200.26
num_responses0.89-0.080.871.000.05-0.140.760.030.69-0.150.80-0.050.190.330.500.090.27
read_ratio-0.050.03-0.070.051.00-0.60-0.020.01-0.20-0.530.270.100.09-0.02-0.12-0.34-0.27
read_time-0.110.36-0.11-0.14-0.601.00-0.08-0.130.030.96-0.24-0.06-0.42-0.22-0.150.190.26
reads0.750.050.760.76-0.02-0.081.000.010.93-0.110.53-0.08-0.010.360.320.220.37
title_word_count0.09-0.310.100.030.01-0.130.011.000.01-0.140.09-0.020.330.130.320.270.24
views0.730.040.750.69-0.200.030.930.011.00-0.010.36-0.06-0.030.330.310.310.41
word_count-0.120.33-0.12-0.15-0.530.96-0.11-0.14-0.011.00-0.230.00-0.38-0.21-0.140.160.17
claps_per_word0.76-0.080.730.800.27-0.240.530.090.36-0.231.00-0.060.240.270.35-0.030.18
editing_days-0.01-0.11-0.00-0.050.10-0.06-0.08-0.02-0.060.00-0.061.000.20-0.000.120.05-0.05
<tag>Education0.27-0.760.270.190.09-0.42-0.010.33-0.03-0.380.240.201.000.380.450.12-0.06
<tag>Data Science0.36-0.430.370.33-0.02-0.220.360.130.33-0.210.27-0.000.381.000.340.270.05
<tag>Towards Data Science0.53-0.390.540.50-0.12-0.150.320.320.31-0.140.350.120.450.341.000.210.19
<tag>Machine Learning0.18-0.110.200.09-0.340.190.220.270.310.16-0.030.050.120.270.211.000.30
<tag>Python0.260.040.260.27-0.270.260.370.240.410.170.18-0.05-0.060.050.190.301.00
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" ], "text/plain": [ " claps days_since_publication fans num_responses \\\n", "claps 1.00 -0.13 0.99 0.89 \n", "days_since_publication -0.13 1.00 -0.14 -0.08 \n", "fans 0.99 -0.14 1.00 0.87 \n", "num_responses 0.89 -0.08 0.87 1.00 \n", "read_ratio -0.05 0.03 -0.07 0.05 \n", "read_time -0.11 0.36 -0.11 -0.14 \n", "reads 0.75 0.05 0.76 0.76 \n", "title_word_count 0.09 -0.31 0.10 0.03 \n", "views 0.73 0.04 0.75 0.69 \n", "word_count -0.12 0.33 -0.12 -0.15 \n", "claps_per_word 0.76 -0.08 0.73 0.80 \n", "editing_days -0.01 -0.11 -0.00 -0.05 \n", "Education 0.27 -0.76 0.27 0.19 \n", "Data Science 0.36 -0.43 0.37 0.33 \n", "Towards Data Science 0.53 -0.39 0.54 0.50 \n", "Machine Learning 0.18 -0.11 0.20 0.09 \n", "Python 0.26 0.04 0.26 0.27 \n", "\n", " read_ratio read_time reads title_word_count \\\n", "claps -0.05 -0.11 0.75 0.09 \n", "days_since_publication 0.03 0.36 0.05 -0.31 \n", "fans -0.07 -0.11 0.76 0.10 \n", "num_responses 0.05 -0.14 0.76 0.03 \n", "read_ratio 1.00 -0.60 -0.02 0.01 \n", "read_time -0.60 1.00 -0.08 -0.13 \n", "reads -0.02 -0.08 1.00 0.01 \n", "title_word_count 0.01 -0.13 0.01 1.00 \n", "views -0.20 0.03 0.93 0.01 \n", "word_count -0.53 0.96 -0.11 -0.14 \n", "claps_per_word 0.27 -0.24 0.53 0.09 \n", "editing_days 0.10 -0.06 -0.08 -0.02 \n", "Education 0.09 -0.42 -0.01 0.33 \n", "Data Science -0.02 -0.22 0.36 0.13 \n", "Towards Data Science -0.12 -0.15 0.32 0.32 \n", "Machine Learning -0.34 0.19 0.22 0.27 \n", "Python -0.27 0.26 0.37 0.24 \n", "\n", " views word_count claps_per_word editing_days \\\n", "claps 0.73 -0.12 0.76 -0.01 \n", "days_since_publication 0.04 0.33 -0.08 -0.11 \n", "fans 0.75 -0.12 0.73 -0.00 \n", "num_responses 0.69 -0.15 0.80 -0.05 \n", "read_ratio -0.20 -0.53 0.27 0.10 \n", "read_time 0.03 0.96 -0.24 -0.06 \n", "reads 0.93 -0.11 0.53 -0.08 \n", "title_word_count 0.01 -0.14 0.09 -0.02 \n", "views 1.00 -0.01 0.36 -0.06 \n", "word_count -0.01 1.00 -0.23 0.00 \n", "claps_per_word 0.36 -0.23 1.00 -0.06 \n", "editing_days -0.06 0.00 -0.06 1.00 \n", "Education -0.03 -0.38 0.24 0.20 \n", "Data Science 0.33 -0.21 0.27 -0.00 \n", "Towards Data Science 0.31 -0.14 0.35 0.12 \n", "Machine Learning 0.31 0.16 -0.03 0.05 \n", "Python 0.41 0.17 0.18 -0.05 \n", "\n", " Education Data Science \\\n", "claps 0.27 0.36 \n", "days_since_publication -0.76 -0.43 \n", "fans 0.27 0.37 \n", "num_responses 0.19 0.33 \n", "read_ratio 0.09 -0.02 \n", "read_time -0.42 -0.22 \n", "reads -0.01 0.36 \n", "title_word_count 0.33 0.13 \n", "views -0.03 0.33 \n", "word_count -0.38 -0.21 \n", "claps_per_word 0.24 0.27 \n", "editing_days 0.20 -0.00 \n", "Education 1.00 0.38 \n", "Data Science 0.38 1.00 \n", "Towards Data Science 0.45 0.34 \n", "Machine Learning 0.12 0.27 \n", "Python -0.06 0.05 \n", "\n", " Towards Data Science Machine Learning \\\n", "claps 0.53 0.18 \n", "days_since_publication -0.39 -0.11 \n", "fans 0.54 0.20 \n", "num_responses 0.50 0.09 \n", "read_ratio -0.12 -0.34 \n", "read_time -0.15 0.19 \n", "reads 0.32 0.22 \n", "title_word_count 0.32 0.27 \n", "views 0.31 0.31 \n", "word_count -0.14 0.16 \n", "claps_per_word 0.35 -0.03 \n", "editing_days 0.12 0.05 \n", "Education 0.45 0.12 \n", "Data Science 0.34 0.27 \n", "Towards Data Science 1.00 0.21 \n", "Machine Learning 0.21 1.00 \n", "Python 0.19 0.30 \n", "\n", " Python \n", "claps 0.26 \n", "days_since_publication 0.04 \n", "fans 0.26 \n", "num_responses 0.27 \n", "read_ratio -0.27 \n", "read_time 0.26 \n", "reads 0.37 \n", "title_word_count 0.24 \n", "views 0.41 \n", "word_count 0.17 \n", "claps_per_word 0.18 \n", "editing_days -0.05 \n", "Education -0.06 \n", "Data Science 0.05 \n", "Towards Data Science 0.19 \n", "Machine Learning 0.30 \n", "Python 1.00 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrs = df[df['type'] == 'published'].corr()\n", "corrs.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we are looking at maximizing claps, what do we want to focus on?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:09.503041Z", "start_time": "2018-12-31T23:49:09.455348Z" } }, "outputs": [ { "data": { "text/plain": [ "claps 1.000000\n", "fans 0.992251\n", "num_responses 0.893159\n", "claps_per_word 0.762564\n", "reads 0.749108\n", "views 0.725450\n", "Towards Data Science 0.533000\n", "Data Science 0.363490\n", "Education 0.267466\n", "Python 0.255534\n", "Machine Learning 0.182377\n", "title_word_count 0.088137\n", "editing_days -0.013607\n", "read_ratio -0.052946\n", "read_time -0.112306\n", "word_count -0.116210\n", "days_since_publication -0.126835\n", "Name: claps, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrs['claps'].sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so most of these occur after the article is released. However, the tag `Towards Data Science` seems to help quite a bit! It also looks like the read time is negatively correlated with the number of claps. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Heatmap\n", "\n", "Using the `plotly` python library, we can very rapidly create interactive great looking charts.\n", "\n", "Here are the avaiable colorscales if you want to try others:\n", "\n", " colorscales = ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n", " 'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n", " 'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:13.876562Z", "start_time": "2018-12-31T23:49:13.825839Z" } }, "outputs": [], "source": [ "colorscales = ['Greys', 'YlGnBu', 'Greens', 'YlOrRd', 'Bluered', 'RdBu',\n", " 'Reds', 'Blues', 'Picnic', 'Rainbow', 'Portland', 'Jet',\n", " 'Hot', 'Blackbody', 'Earth', 'Electric', 'Viridis', 'Cividis']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = ff.create_annotated_heatmap(z = corrs.round(2).values, \n", " x =list(corrs.columns), \n", " y=list(corrs.index), \n", " colorscale='Portland',\n", " annotation_text=corrs.round(2).values)\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlations by themselves don't tell us that much. It does not help that most of these are pretty obvious, such as the `claps` and `fans` will be highly correlated. Sometimes correlations by themselves are useful, but not really in this case." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatterplot Matrix" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:18.488935Z", "start_time": "2018-12-31T23:49:17.928856Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "rgb(0, 0, 131)" }, "showlegend": false, "type": "histogram", "uid": "1f2666b0-969e-4e12-8a44-97c8eb371452", "x": [ 7, 14, 42, 9, 14, 47, 14, 12, 7, 17, 38, 31, 12, 29, 27, 6, 31, 6, 24, 8, 21, 11, 9, 19, 17, 15, 5, 11, 12, 14, 6, 11, 5, 6, 12, 6, 7, 4, 6, 11, 6, 10, 3, 6, 12, 3, 9, 8, 4, 11, 9, 11, 10, 11, 10, 8, 10, 12, 12, 7, 3, 15, 13, 11, 9, 12, 11, 20, 10, 14, 9, 18, 15, 6, 11, 8, 10, 3, 17, 15, 1, 16, 10, 15, 5, 8, 17, 9, 8, 13, 10, 9, 11, 6, 15, 9, 6, 8, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = ff.create_scatterplotmatrix(df[['read_time', 'views', 'read_ratio', 'publication']],\n", " index = 'publication', \n", " diag='histogram', \n", " size=8, width=1000, height=1000,\n", " title='Scatterplot Matrix by Publication')\n", "\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Histograms" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:21.639684Z", "start_time": "2018-12-31T23:49:20.688276Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from visuals import make_hist" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:21.967586Z", "start_time": "2018-12-31T23:49:21.642984Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "name": "Engineering @ Feature Labs", "type": "histogram", "uid": "c3afdf35-f9e1-4573-8b74-2cbe442013d2", "x": [ 2721 ] }, { "name": "None", "type": "histogram", "uid": "dbe505cd-7d11-4ac5-8e05-5969feb09311", "x": [ 161, 157, 1050, 52, 56, 303, 9294, 23050, 119, 61, 26901, 145, 53, 5253, 4651, 7941, 12596, 131, 2104, 35, 80120, 159, 312, 3255, 147, 105, 1557, 226, 811, 403, 251, 681, 197, 3, 104, 7, 81, 131, 25, 973 ] }, { "name": "Noteworthy - The Journal Blog", "type": "histogram", "uid": "eec9bae4-337d-4084-bd88-1efc777e94a8", "x": [ 2025 ] }, { "name": "Towards Data Science", "type": "histogram", "uid": "ea27afab-33e4-4946-ad94-8f77fd0bf723", "x": [ 159312, 31246, 2679, 2329, 104762, 123703, 76296, 4752, 124926, 18889, 3981, 15657, 31872, 15061, 46576, 47481, 2944, 53498, 33735, 4575, 12704, 115191, 58296, 51564, 27642, 104858, 51807, 44925, 54169, 20621, 23364, 66076, 2360, 119692, 44429, 25969, 32749, 41655, 108531, 10526, 75486, 23269, 29257, 41650, 5301, 3926, 23370, 16427, 3891, 24218, 42197, 6128, 1639, 12461, 14575, 23997, 3563, 1558, 23410, 25683, 6108, 12611, 32457, 3136, 30062, 4960, 6132, 5119, 7771, 26384, 14084, 7575, 20563, 21563, 21997, 4335, 8573, 4740, 870 ] } ], "layout": { "title": "Views Distribution by Publication", "xaxis": { "title": "Views" }, "yaxis": { "title": "Count" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = make_hist(df, x='views', category='publication')\n", "iplot(figure)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:22.238888Z", "start_time": "2018-12-31T23:49:21.972583Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "name": "published", "type": "histogram", "uid": "b3a4adb0-8696-4b2f-9b6f-cef26d031015", "x": [ 1859, 3891, 12025, 2533, 3892, 13048, 1778, 2345, 1895, 4684, 6666, 7659, 3341, 5508, 4772, 1214, 6800, 1412, 3904, 2264, 4494, 2850, 2509, 5160, 3504, 3569, 1073, 2817, 2456, 2974, 1314, 2395, 1169, 1557, 2528, 1361, 1653, 1014, 1562, 2480, 1281, 2450, 721, 1375, 2772, 634, 1944, 1827, 933, 2565, 1906, 2394, 2220, 2614, 2365, 1577, 2394, 2690, 2620, 1480, 765, 3553, 3106, 2622, 2338, 2975, 2648, 5228, 1998, 3393, 1957, 4298, 3718, 1247, 2634, 2041, 2343, 373, 3996, 3834, 163, 4042, 2658, 3756, 1172, 1580, 3756, 2087, 1809, 2999, 2483, 2172, 2933, 1346, 3797, 1979, 1286, 1741, 2230, 1561, 1355, 3398, 2824, 1397, 877, 1310, 1410, 1806, 1075, 2185, 7125, 1898 ] }, { "name": "unlisted", "type": "histogram", "uid": "f73230e3-c0bc-4baf-a9e1-c1d5de1b11ab", "x": [ 2128, 1918, 15063, 3659, 9698, 4145, 8507, 3068, 12083 ] } ], "layout": { "title": "Word Count Distribution by Type", "xaxis": { "title": "Word Count" }, "yaxis": { "title": "Count" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = make_hist(df, x='word_count', category='type')\n", "iplot(figure)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:22.595325Z", "start_time": "2018-12-31T23:49:22.243364Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "type": "histogram", "uid": "22cbb5f0-61f8-438d-867c-184be9cfd7bb", "x": [ 2, 18, 50, 0, 0, 0, 73, 233, 2, 0, 682, 5, 7, 87, 23, 4, 113, 8, 17, 8, 4700, 2600, 72, 123, 856, 186, 11, 119, 2000, 4100, 59, 2700, 77, 14, 6900, 1200, 274, 1600, 1600, 1000, 4300, 4970, 12900, 223, 4800, 101, 5900, 545, 895, 6700, 6500, 2700, 1100, 2900, 2600, 2800, 5300, 1300, 1000, 7800, 364, 13500, 4100, 2200, 3800, 6200, 8200, 1300, 5400, 2300, 2700, 2100, 707, 632, 2600, 4000, 822, 825, 3700, 651, 657, 1100, 2800, 3100, 787, 222, 3300, 305, 2500, 924, 2000, 980, 3200, 649, 2300, 806, 743, 581, 806, 2200, 1300, 619, 2600, 2000, 3500, 420, 705, 809, 205, 482, 120, 336, 26, 0, 24, 0, 5, 6, 0, 353, 216 ] } ], "layout": { "title": "Claps Distribution", "xaxis": { "title": "Claps" }, "yaxis": { "title": "Count" } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = make_scatter_plot(df, x='read_time', y='views', ylog=True,\n", " scale='read_ratio', sizeref=0.2)\n", "iplot(figure)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:26.060916Z", "start_time": "2018-12-31T23:49:25.958150Z" } }, "outputs": [], "source": [ "df['binned_ratio'] = pd.cut(df['read_ratio'], list(range(0, 100, 10))).astype('str')\n", "df['binned_claps'] = pd.cut(df['claps'], list(np.insert(np.logspace(start=0, stop=5, num=6),0,-1).astype(int))).astype(str)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:26.253780Z", "start_time": "2018-12-31T23:49:26.063960Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": [ 657, 364, 12900, 101, 825, 1600, 895, 11, 787, 3500, 482, 77, 743, 14, 1200, 1600, 4300, 705, 8, 4, 632, 1300, 649, 223, 59, 2200, 420, 7800, 274, 2, 2, 4000, 336, 120, 205, 2000, 581, 2500, 222, 2800, 545, 8, 216, 2700, 806, 980, 72, 305, 0, 809, 3800, 806, 5900, 6500, 1100, 2600, 2000, 5300, 2800, 4970, 822, 5400, 3200, 2600, 2700, 2700, 6700, 2600, 8200, 2900, 1000, 2200, 119, 7, 4800, 233, 1300, 2600, 6900, 1000, 2000, 6200, 924, 0, 4100, 4100, 6, 2300, 73, 18, 0, 707, 619, 3100, 651, 186, 2300, 13500, 1100, 3300, 0, 3700, 856, 2100, 123, 1300, 4700, 0, 17, 23, 353, 87, 5, 113, 24, 682, 50, 5, 0, 0, 26 ], "colorscale": "Viridis", "line": { "color": "black", "width": 0.5 }, "opacity": 0.8, "showscale": true, "size": [ 657, 364, 12900, 101, 825, 1600, 895, 11, 787, 3500, 482, 77, 743, 14, 1200, 1600, 4300, 705, 8, 4, 632, 1300, 649, 223, 59, 2200, 420, 7800, 274, 2, 2, 4000, 336, 120, 205, 2000, 581, 2500, 222, 2800, 545, 8, 216, 2700, 806, 980, 72, 305, 0, 809, 3800, 806, 5900, 6500, 1100, 2600, 2000, 5300, 2800, 4970, 822, 5400, 3200, 2600, 2700, 2700, 6700, 2600, 8200, 2900, 1000, 2200, 119, 7, 4800, 233, 1300, 2600, 6900, 1000, 2000, 6200, 924, 0, 4100, 4100, 6, 2300, 73, 18, 0, 707, 619, 3100, 651, 186, 2300, 13500, 1100, 3300, 0, 3700, 856, 2100, 123, 1300, 4700, 0, 17, 23, 353, 87, 5, 113, 24, 682, 50, 5, 0, 0, 26 ], "sizemin": 2, "sizemode": "area", "sizeref": 5 }, "mode": "markers", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "If your files are saved only on your laptop they might as well not exist!", "Python is the Perfect Tool for any Problem", "Slow Tech: Take Back Your Mind", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Learn By Sharing", "Unintended Consequences and Goodhart’s Law", "The Simple Science of Global Warming", "Five Minutes to Your Own Website", "Jupyter Notebook Extensions", "Docker for Data Science Without the Hassle", "A Review of the Coursera Machine Learning Specialization", "How to Create Value with Machine Learning", "The Perils of Rare Events", "Correlation vs. Causation: An Example", "Overfitting vs. Underfitting: A Conceptual Explanation", "How to Master New Skills", "Please Steal My Articles", "The Worst They Can Say is No", "Controlling your Location in Google Chrome", "How to get the right data? Trying asking for it.", "Deploying a Python Web App on AWS", "Overcome Your Biases with Data", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "The Failures of Common Sense", "Deploying a Keras Deep Learning Model as a Web Application in Python", "How to Write a Jupyter Notebook Extension", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "Real Life Superpowers", "Screw the Environment, but Consider Your Wallet", "Make an Effort, Not an Excuse", "The most important part of a data science project is writing a blog post", "What I learned in 2018", "On Blame", "Stop Regretting the Present", "Python and Slack: A Natural Match", "Feature Engineering: What Powers Machine Learning", "Neural Network Embeddings Explained", "Converting Medium Posts to Markdown for Your Blog", "Visualizing Data with Pairs Plots in Python", "Data Visualization Hackathon Style", "The Case for Criticism", "The Copernican Principle and How to Use Statistics to 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = make_scatter_plot(df, x='word_count', y='reads', xlog=True,\n", " scale='claps', sizeref=3)\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Univariate Linear Regressions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the linear regressions, we'll focus on articles that were published in Towards Data Science. This makes the relationships clearer because the other articles are a mixed bag. We'll start off using a single variable - univariate - and focusing on linear relationships." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:28.325906Z", "start_time": "2018-12-31T23:49:28.091900Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "line": { "color": "black" }, "opacity": 0.8, "size": 12 }, "mode": "markers", "name": "observations", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Python is the Perfect Tool for any Problem", "If your files are saved only on your laptop they might as well not exist!", "Jupyter Notebook Extensions", "Unintended Consequences and Goodhart’s Law", "Learn By Sharing", "Docker for Data Science Without the Hassle", "A Review of the Coursera Machine Learning Specialization", "Five Minutes to Your Own Website", "How to get the right data? Trying asking for it.", "How to Master New Skills", "How to Create Value with Machine Learning", "How to Write a Jupyter Notebook Extension", "Overcome Your Biases with Data", "Deploying a Python Web App on AWS", "Correlation vs. Causation: An Example", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "Python and Slack: A Natural Match", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Overfitting vs. Underfitting: A Conceptual Explanation", "Visualizing Data with Pairs Plots in Python", "Converting Medium Posts to Markdown for Your Blog", "Feature Engineering: What Powers Machine Learning", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Neural Network Embeddings Explained", "Data Visualization Hackathon Style", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "Controlling the Web 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Same Data", "Stock Prediction in Python", "Beyond Accuracy: Precision and Recall", "Histograms and Density Plots in Python", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "A Complete Machine Learning Walk-Through in Python: Part Three", "Why Automated Feature Engineering Will Change the Way You Do Machine Learning", "Automated Feature Engineering in Python", "Practical Advice for Data Science Writing", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Markov Chain Monte Carlo in Python", "A Theory of Prediction", "Estimating Probabilities with Bayesian Modeling in Python", "My Weaknesses as a Data Scientist", "Time Series Analysis in Python: An Introduction", "Machine Learning Kaggle Competition Part One: Getting Started", "Building a Recommendation System Using Neural Network Embeddings", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "Transfer Learning with Convolutional Neural Networks in PyTorch", "Improving the Random Forest in Python Part 1", "A Complete Machine Learning Project Walk-Through in Python: Part One", "Data Science: A Personal Application", "Machine Learning Kaggle Competition: Part Three Optimization", "Another Machine Learning Walk-Through and a Challenge", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "Recurrent Neural Networks by Example in Python", "A “Data Science for Good” Machine Learning Project Walk-Through in Python: Part Two", "A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One", "An Implementation and Explanation of the Random Forest in Python", "Automated Machine Learning Hyperparameter Tuning in Python", "Random Forest in Python", "Machine Learning Kaggle Competition Part Two: Improving" ], "type": "scatter", "uid": "10db071c-44b1-439f-9c9b-1a8cfab3d28c", "x": [ 163, 373, 721, 765, 877, 933, 1014, 1075, 1169, 1172, 1247, 1281, 1286, 1310, 1346, 1355, 1361, 1375, 1397, 1480, 1561, 1562, 1577, 1580, 1741, 1806, 1809, 1827, 1898, 1906, 1944, 1957, 1979, 1998, 2041, 2220, 2230, 2338, 2343, 2365, 2394, 2394, 2395, 2450, 2456, 2480, 2483, 2528, 2565, 2614, 2620, 2622, 2634, 2648, 2658, 2690, 2772, 2817, 2824, 2933, 2974, 2975, 2999, 3106, 3393, 3398, 3504, 3553, 3569, 3718, 3756, 3756, 3797, 3834, 3996, 4042, 4298, 4494, 5228 ], "y": [ 1639, 24218, 47481, 2360, 21997, 12704, 3981, 4740, 4752, 3563, 3926, 15061, 6132, 4335, 3136, 14084, 18889, 2944, 21563, 66076, 26384, 15657, 44925, 1558, 5119, 8573, 25683, 4575, 870, 58296, 33735, 29257, 4960, 75486, 16427, 27642, 7771, 32749, 3891, 51807, 51564, 54169, 76296, 46576, 104762, 31872, 12611, 124926, 115191, 104858, 23364, 25969, 23370, 108531, 14575, 20621, 53498, 2329, 20563, 32457, 123703, 41655, 6108, 44429, 23269, 7575, 31246, 119692, 2679, 5301, 23997, 23410, 30062, 6128, 42197, 12461, 41650, 159312, 10526 ] } ], "layout": { "font": { "size": 14 }, "title": "Views vs Word Count", "xaxis": { "title": "Word Count" }, "yaxis": { "title": "Views" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tds = df[df['publication'] == 'Towards Data Science'].copy()\n", "figure = make_scatter_plot(tds, 'word_count', 'views')\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Views Regressed by Word Count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do a regression of the number of words versus the views for articles published in towards data science. We are using `statsmodels.api.OLS` which sets the intercept to be 0. I made this choice because the number of views can never be negative (sometimes we do need an intercept so I left this as a parameter)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:30.623042Z", "start_time": "2018-12-31T23:49:30.535062Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: views R-squared: 0.502
Model: OLS Adj. R-squared: 0.495
Method: Least Squares F-statistic: 78.50
Date: Mon, 31 Dec 2018 Prob (F-statistic): 2.02e-13
Time: 17:49:30 Log-Likelihood: -935.54
No. Observations: 79 AIC: 1873.
Df Residuals: 78 BIC: 1875.
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
word_count 13.3585 1.508 8.860 0.000 10.357 16.360
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 17.663 Durbin-Watson: 1.804
Prob(Omnibus): 0.000 Jarque-Bera (JB): 20.864
Skew: 1.166 Prob(JB): 2.95e-05
Kurtosis: 3.949 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: views R-squared: 0.502\n", "Model: OLS Adj. R-squared: 0.495\n", "Method: Least Squares F-statistic: 78.50\n", "Date: Mon, 31 Dec 2018 Prob (F-statistic): 2.02e-13\n", "Time: 17:49:30 Log-Likelihood: -935.54\n", "No. Observations: 79 AIC: 1873.\n", "Df Residuals: 78 BIC: 1875.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "word_count 13.3585 1.508 8.860 0.000 10.357 16.360\n", "==============================================================================\n", "Omnibus: 17.663 Durbin-Watson: 1.804\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 20.864\n", "Skew: 1.166 Prob(JB): 2.95e-05\n", "Kurtosis: 3.949 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import statsmodels.api as sm\n", "\n", "lin_reg=sm.OLS(tds['views'], tds['word_count']).fit()\n", "lin_reg.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tells us that for every extra word, I get 13 more views! If we look at the plot, there is one outlying data point beyond 5000 words. What happens if I stick to articles under 5000 words published on Towards Data Science?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:31.791350Z", "start_time": "2018-12-31T23:49:31.669836Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: views R-squared: 0.522
Model: OLS Adj. R-squared: 0.516
Method: Least Squares F-statistic: 84.10
Date: Mon, 31 Dec 2018 Prob (F-statistic): 5.62e-14
Time: 17:49:31 Log-Likelihood: -922.54
No. Observations: 78 AIC: 1847.
Df Residuals: 77 BIC: 1849.
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
word_count 14.0089 1.528 9.171 0.000 10.967 17.051
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 18.017 Durbin-Watson: 1.588
Prob(Omnibus): 0.000 Jarque-Bera (JB): 21.482
Skew: 1.204 Prob(JB): 2.16e-05
Kurtosis: 3.902 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: views R-squared: 0.522\n", "Model: OLS Adj. R-squared: 0.516\n", "Method: Least Squares F-statistic: 84.10\n", "Date: Mon, 31 Dec 2018 Prob (F-statistic): 5.62e-14\n", "Time: 17:49:31 Log-Likelihood: -922.54\n", "No. Observations: 78 AIC: 1847.\n", "Df Residuals: 77 BIC: 1849.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "word_count 14.0089 1.528 9.171 0.000 10.967 17.051\n", "==============================================================================\n", "Omnibus: 18.017 Durbin-Watson: 1.588\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 21.482\n", "Skew: 1.204 Prob(JB): 2.16e-05\n", "Kurtosis: 3.902 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tds_clean = tds[tds['word_count'] < 5000].copy()\n", "lin_reg = sm.OLS(tds_clean['views'], tds_clean['word_count']).fit()\n", "lin_reg.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we see that for every extra word, I get 14 more views! However, it looks like I want to keep my articles under 5000 words (about a 25 minute reading time). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read Ratio Regressed by Reading Time\n", "\n", "If we want to fit a model with an intercept, we can use `scipy.stats.linregress`" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:34.319248Z", "start_time": "2018-12-31T23:49:34.127296Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "line": { "color": "black" }, "opacity": 0.8, "size": 12 }, "mode": "markers", "name": "observations", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Python is the Perfect Tool for any Problem", "If your files are saved only on your laptop they might as well not exist!", "Unintended Consequences and Goodhart’s Law", "Learn By Sharing", "A Review of the Coursera Machine Learning Specialization", "Five Minutes to Your Own Website", "Jupyter Notebook Extensions", "Docker for Data Science Without the Hassle", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "How to get the right data? Trying asking for it.", "How to Master New Skills", "How to Create Value with Machine Learning", "Overfitting vs. Underfitting: A Conceptual Explanation", "Overcome Your Biases with Data", "Deploying a Python Web App on AWS", "Correlation vs. Causation: An Example", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "How to Write a Jupyter Notebook Extension", "Python and Slack: A Natural Match", "Visualizing Data with Pairs Plots in Python", "Converting Medium Posts to Markdown for Your Blog", "Feature Engineering: What Powers Machine Learning", "Neural Network Embeddings Explained", "Data Visualization Hackathon Style", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "The most important part of a data science project is writing a blog post", "Automated Machine Learning on the Cloud in Python", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "Controlling the Web with Python", "Bayes’ Rule Applied", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "Practical Advice for Data Science Writing", "Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data", "Statistical Significance Explained", "Introduction to Bayesian Linear Regression", "Parallelizing Feature Engineering with Dask", "Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard", "A Feature Selection Tool for Machine Learning in Python", "Data Visualization with Bokeh in Python, Part II: Interactions", "Data Visualization with Bokeh in Python, Part I: Getting Started", "A Theory of Prediction", "Automated Feature Engineering in Python", "Why Automated Feature Engineering Will Change the Way You Do Machine Learning", "A Complete Machine Learning Walk-Through in Python: Part Three", "My Weaknesses as a Data Scientist", "Beyond Accuracy: Precision and Recall", "Overfitting vs. Underfitting: A Complete Example", "Histograms and Density Plots in Python", "Stock Analysis in Python", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "Stock Prediction in Python", "Hyperparameter Tuning the Random Forest in Python", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Markov Chain Monte Carlo in Python", "Estimating Probabilities with Bayesian Modeling in Python", "Machine Learning Kaggle Competition Part One: Getting Started", "Building a Recommendation System Using Neural Network Embeddings", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "Time Series Analysis in Python: An Introduction", "Transfer Learning with Convolutional Neural Networks in PyTorch", "A Complete Machine Learning Project Walk-Through in Python: Part One", "Data Science: A Personal Application", "Machine Learning Kaggle Competition: Part Three Optimization", "Another Machine Learning Walk-Through and a Challenge", "Recurrent Neural Networks by Example in Python", "A “Data Science for Good” Machine Learning Project Walk-Through in Python: Part Two", "An Implementation and Explanation of the Random Forest in Python", "Improving the Random Forest in Python Part 1", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One", "Automated Machine Learning Hyperparameter Tuning in Python", "Random Forest in Python" ], "type": "scatter", "uid": "a38fcb11-4509-4cd9-8017-45c65ec36241", "x": [ 1, 3, 3, 3, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, 15, 15, 15, 15, 16, 17, 17, 17, 18, 21 ], "y": [ 74.5, 44.18, 62.61, 63.31, 43.46, 54.81, 62.1, 40.58, 46, 29.58, 43.34, 43.45, 47.27, 38.57, 41.88, 40.47, 34.05, 35.89, 30.31, 31.54, 21.71, 19.3, 31.34, 18.61, 21.37, 27.09, 42.8, 28.74, 33.17, 25.02, 21.43, 24.17, 22.75, 32.23, 29.37, 26.86, 26.97, 23.81, 29.82, 33.68, 26.83, 33.13, 25.46, 36.97, 36.44, 31.64, 21.72, 24.75, 23.49, 28.55, 20.42, 30.72, 27.15, 26.42, 26.46, 31.1, 23.99, 30.75, 28.78, 22.12, 23.82, 19.78, 20.61, 21.57, 24.99, 17.52, 18.86, 28.89, 18.02, 19, 17.25, 19.3, 17.29, 22.69, 18.21, 16.75, 14.35, 17.67 ] } ], "layout": { "font": { "size": 14 }, "title": "Read Ratio vs Read Time", "xaxis": { "title": "Read Time" }, "yaxis": { "title": "Read Ratio" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = make_scatter_plot(tds_clean, 'read_time', 'read_ratio')\n", "iplot(figure)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:34.411017Z", "start_time": "2018-12-31T23:49:34.322733Z" } }, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=-2.3226617522329582, intercept=53.29509659584714, rvalue=-0.7752588331903641, pvalue=7.99685522357628e-17, stderr=0.21707239892501062)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "stats.linregress(tds_clean['read_time'], tds_clean['read_ratio'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time, we see that for every additional minute of reading time, the percentage of people who read the article declines by 2.3%. For an article with a 0 minute reading time, 53% of people will read it! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at a few different fits." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:36.879822Z", "start_time": "2018-12-31T23:49:36.700938Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "line": { "color": "black" }, "opacity": 0.8, "size": 12 }, "mode": "markers", "name": "observations", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Python is the Perfect Tool for any Problem", "If your files are saved only on your laptop they might as well not exist!", "Jupyter Notebook Extensions", "Unintended Consequences and Goodhart’s Law", "Learn By Sharing", "Docker for Data Science Without the Hassle", "A Review of the Coursera Machine Learning Specialization", "Five Minutes to Your Own Website", "How to get the right data? Trying asking for it.", "How to Master New Skills", "How to Create Value with Machine Learning", "How to Write a Jupyter Notebook Extension", "Overcome Your Biases with Data", "Deploying a Python Web App on AWS", "Correlation vs. Causation: An Example", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "Python and Slack: A Natural Match", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Overfitting vs. Underfitting: A Conceptual Explanation", "Visualizing Data with Pairs Plots in Python", "Converting Medium Posts to Markdown for Your Blog", "Feature Engineering: What Powers Machine Learning", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Neural Network Embeddings Explained", "Data Visualization Hackathon Style", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "Controlling the Web with Python", "Bayes’ Rule Applied", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "A Feature Selection Tool for Machine Learning in Python", "The most important part of a data science project is writing a blog post", "Data Visualization with Bokeh in Python, Part II: Interactions", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Automated Machine Learning on the Cloud in Python", "Parallelizing Feature Engineering with Dask", "Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard", "Data Visualization with Bokeh in Python, Part I: Getting Started", "Introduction to Bayesian Linear Regression", "Stock Analysis in Python", "Statistical Significance Explained", "Hyperparameter Tuning the Random Forest in Python", "Overfitting vs. Underfitting: A Complete Example", "Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data", "Stock Prediction in Python", "Beyond Accuracy: Precision and Recall", "Histograms and Density Plots in Python", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2", "A Complete Machine Learning Walk-Through in Python: Part Three", "Why Automated Feature Engineering Will Change the Way You Do Machine Learning", "Automated Feature Engineering in Python", "Practical Advice for Data Science Writing", "Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 1", "Markov Chain Monte Carlo in Python", "A Theory of Prediction", "Estimating Probabilities with Bayesian Modeling in Python", "My Weaknesses as a Data Scientist", "Time Series Analysis in Python: An Introduction", "Machine Learning Kaggle Competition Part One: Getting Started", "Building a Recommendation System Using Neural Network Embeddings", "A Complete Machine Learning Walk-Through in Python: Part Two", "A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning", "Transfer Learning with Convolutional Neural Networks in PyTorch", "Improving the Random Forest in Python Part 1", "A Complete Machine Learning Project Walk-Through in Python: Part One", "Data Science: A Personal Application", "Machine Learning Kaggle Competition: Part Three Optimization", "Another Machine Learning Walk-Through and a Challenge", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "Recurrent Neural Networks by Example in Python", "A “Data Science for Good” Machine Learning Project Walk-Through in Python: Part Two", "A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One", "An Implementation and Explanation of the Random Forest in Python", "Automated Machine Learning Hyperparameter Tuning in Python", "Random Forest in Python" ], "type": "scatter", "uid": "33ae671e-e638-407e-8396-dc7b24af0094", "x": [ 163, 373, 721, 765, 877, 933, 1014, 1075, 1169, 1172, 1247, 1281, 1286, 1310, 1346, 1355, 1361, 1375, 1397, 1480, 1561, 1562, 1577, 1580, 1741, 1806, 1809, 1827, 1898, 1906, 1944, 1957, 1979, 1998, 2041, 2220, 2230, 2338, 2343, 2365, 2394, 2394, 2395, 2450, 2456, 2480, 2483, 2528, 2565, 2614, 2620, 2622, 2634, 2648, 2658, 2690, 2772, 2817, 2824, 2933, 2974, 2975, 2999, 3106, 3393, 3398, 3504, 3553, 3569, 3718, 3756, 3756, 3797, 3834, 3996, 4042, 4298, 4494 ], "y": [ 1639, 24218, 47481, 2360, 21997, 12704, 3981, 4740, 4752, 3563, 3926, 15061, 6132, 4335, 3136, 14084, 18889, 2944, 21563, 66076, 26384, 15657, 44925, 1558, 5119, 8573, 25683, 4575, 870, 58296, 33735, 29257, 4960, 75486, 16427, 27642, 7771, 32749, 3891, 51807, 51564, 54169, 76296, 46576, 104762, 31872, 12611, 124926, 115191, 104858, 23364, 25969, 23370, 108531, 14575, 20621, 53498, 2329, 20563, 32457, 123703, 41655, 6108, 44429, 23269, 7575, 31246, 119692, 2679, 5301, 23997, 23410, 30062, 6128, 42197, 12461, 41650, 159312 ] }, { "line": { "dash": "dash" }, "marker": { "opacity": 0.6, "size": 8 }, "mode": "lines+markers", "name": "fit_values", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Python is the Perfect Tool for any Problem", "If your files are saved only on your laptop they might as well not exist!", "Jupyter Notebook Extensions", "Unintended Consequences and Goodhart’s Law", "Learn By Sharing", "Docker for Data Science Without the Hassle", "A Review of the Coursera Machine Learning Specialization", "Five Minutes to Your Own Website", "How to get the right data? Trying asking for it.", "How to Master New Skills", "How to Create Value with Machine Learning", "How to Write a Jupyter Notebook Extension", "Overcome Your Biases with Data", "Deploying a Python Web App on AWS", "Correlation vs. Causation: An Example", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "Python and Slack: A Natural Match", "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Overfitting vs. Underfitting: A Conceptual Explanation", "Visualizing Data with Pairs Plots in Python", "Converting Medium Posts to Markdown for Your Blog", "Feature Engineering: What Powers Machine Learning", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Neural Network Embeddings Explained", "Data Visualization Hackathon Style", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "Controlling the Web with Python", "Bayes’ Rule Applied", "An Introductory Example of Bayesian Optimization in Python with Hyperopt", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "A Feature Selection Tool for Machine Learning in Python", "The most important part of a data science project is writing a blog post", "Data Visualization with Bokeh in Python, Part II: Interactions", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Automated Machine Learning on the Cloud in Python", "Parallelizing Feature Engineering with Dask", "Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard", "Data Visualization with Bokeh in Python, Part I: Getting Started", "Introduction to Bayesian Linear Regression", "Stock Analysis in Python", "Statistical Significance Explained", "Hyperparameter Tuning the Random Forest in Python", "Overfitting vs. Underfitting: A Complete Example", "Simpson’s Paradox: How to Prove Opposite Arguments with the 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"size": 32 }, "showarrow": false, "text": "$views = 14.01 * word count$", "x": 3370.5, "y": 143380.80000000002 } ], "font": { "size": 14 }, "title": "Views vs Word Count with Fit", "xaxis": { "title": "Word Count" }, "yaxis": { "title": "Views" } } }, "text/html": [ "
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OLS Regression Results
Dep. Variable: views R-squared: 0.522
Model: OLS Adj. R-squared: 0.516
Method: Least Squares F-statistic: 84.10
Date: Mon, 31 Dec 2018 Prob (F-statistic): 5.62e-14
Time: 17:49:36 Log-Likelihood: -922.54
No. Observations: 78 AIC: 1847.
Df Residuals: 77 BIC: 1849.
Df Model: 1
Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err t P>|t| [0.025 0.975]
word_count 14.0089 1.528 9.171 0.000 10.967 17.051
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Omnibus: 18.017 Durbin-Watson: 1.937
Prob(Omnibus): 0.000 Jarque-Bera (JB): 21.482
Skew: 1.204 Prob(JB): 2.16e-05
Kurtosis: 3.902 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: views R-squared: 0.522\n", "Model: OLS Adj. R-squared: 0.516\n", "Method: Least Squares F-statistic: 84.10\n", "Date: Mon, 31 Dec 2018 Prob (F-statistic): 5.62e-14\n", "Time: 17:49:36 Log-Likelihood: -922.54\n", "No. Observations: 78 AIC: 1847.\n", "Df Residuals: 77 BIC: 1849.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "word_count 14.0089 1.528 9.171 0.000 10.967 17.051\n", "==============================================================================\n", "Omnibus: 18.017 Durbin-Watson: 1.937\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 21.482\n", "Skew: 1.204 Prob(JB): 2.16e-05\n", "Kurtosis: 3.902 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:37.127861Z", "start_time": "2018-12-31T23:49:36.962439Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "line": { "color": "black" }, "opacity": 0.8, "size": 12 }, "mode": "markers", "name": "observations", "text": [ "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Python is the Perfect Tool for any Problem", "If your files are saved only on your laptop they might as well not exist!", "Unintended Consequences and Goodhart’s Law", "Learn By Sharing", "A Review of the Coursera Machine Learning Specialization", "Five Minutes to Your Own Website", "Jupyter Notebook Extensions", "Docker for Data Science Without the Hassle", "The Misleading Effect of Noise: The Multiple Comparisons Problem", "How to get the right data? 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OLS Regression Results
Dep. Variable: fans R-squared: 0.403
Model: OLS Adj. R-squared: 0.396
Method: Least Squares F-statistic: 52.05
Date: Mon, 31 Dec 2018 Prob (F-statistic): 3.25e-10
Time: 17:49:37 Log-Likelihood: -603.60
No. Observations: 78 AIC: 1209.
Df Residuals: 77 BIC: 1212.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
title_word_count 51.7363 7.171 7.215 0.000 37.457 66.015
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Omnibus: 22.055 Durbin-Watson: 2.394
Prob(Omnibus): 0.000 Jarque-Bera (JB): 29.671
Skew: 1.267 Prob(JB): 3.61e-07
Kurtosis: 4.645 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: fans R-squared: 0.403\n", "Model: OLS Adj. R-squared: 0.396\n", "Method: Least Squares F-statistic: 52.05\n", "Date: Mon, 31 Dec 2018 Prob (F-statistic): 3.25e-10\n", "Time: 17:49:37 Log-Likelihood: -603.60\n", "No. Observations: 78 AIC: 1209.\n", "Df Residuals: 77 BIC: 1212.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "====================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------------\n", "title_word_count 51.7363 7.171 7.215 0.000 37.457 66.015\n", "==============================================================================\n", "Omnibus: 22.055 Durbin-Watson: 2.394\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 29.671\n", "Skew: 1.267 Prob(JB): 3.61e-07\n", "Kurtosis: 4.645 Cond. No. 1.00\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This clearly is not the best fit! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Univariate Polynomial Regressions\n", "\n", "Next, we'll let the degree of the fit increase above 1. Overfitting (especially with limited data) is definitely going to be the outcome, but we'll let this serve as a lesson about having too many parameters in your model! " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:38.999175Z", "start_time": "2018-12-31T23:49:38.930947Z" } }, "outputs": [], "source": [ "from visuals import make_poly_fits" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:39.323592Z", "start_time": "2018-12-31T23:49:39.001543Z" } }, "outputs": [ { "data": { "text/html": [ "
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fitrmseparams
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" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure, fig_stats = make_poly_fits(tds_clean, x='title_word_count', y='fans', degree=10)\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Multivariate Regressions\n", "\n", "Next, we'll consider more independent variables in our model. For this, we need to break out the exceptional Scikit-Learn library. We'll use `liner_model.LinearRegression` which supports multiple independent variables." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:40.414757Z", "start_time": "2018-12-31T23:49:40.295466Z" } }, "outputs": [ { "data": { "text/plain": [ "['read_time',\n", " 'editing_days',\n", " 'title_word_count',\n", " 'Education',\n", " 'Data Science',\n", " 'Towards Data Science',\n", " 'Machine Learning',\n", " 'Python']" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error\n", "\n", "x = ['read_time', 'editing_days', 'title_word_count']\n", "x.extend(c for c in df.columns if '' in c)\n", "x" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:40.538349Z", "start_time": "2018-12-31T23:49:40.418531Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning:\n", "\n", "internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", "\n" ] }, { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin_model = LinearRegression()\n", "lin_model.fit(tds[x], tds['reads'])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:50:23.780914Z", "start_time": "2018-12-31T23:50:23.708706Z" } }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin_model = LinearRegression()\n", "lin_model.fit(tds[x], tds['reads'])\n", "\n", "slopes, intercept, = lin_model.coef_, lin_model.intercept_\n", "fit = lin_model.predict(tds[x])\n", "r2 = lin_model.score(tds[x], tds['reads'])\n", "rmse = np.sqrt(mean_squared_error(y_true=tds['reads'], y_pred=fit))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:40.628306Z", "start_time": "2018-12-31T23:49:40.542217Z" } }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Independent Variable: Read Time Slope: -104.48\n", "Independent Variable: Editing Days Slope: -273.31\n", "Independent Variable: Title Word Count Slope: -513.13\n", "Independent Variable: Education Slope: -6884.35\n", "Independent Variable: Data Science Slope: 1751.44\n", "Independent Variable: Towards Data Science Slope: 3874.60\n", "Independent Variable: Machine Learning Slope: 1027.65\n", "Independent Variable: Python Slope: 7062.91\n", "Intercept: 13383.85\n", "\n", "Coefficient of Determination: 0.37\n", "RMSE: 6934.72\n" ] } ], "source": [ "for p, s in zip(x, slopes):\n", " print(f'Independent Variable: {p.replace(\"_\", \" \").title():25} Slope: {s:.2f}')\n", "\n", "print(f'Intercept: {intercept:.2f}')\n", "print(f'\\nCoefficient of Determination: {r2:.2f}')\n", "print(f'RMSE: {rmse:.2f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that some variables contribute positively to the number of reads, while others decrease the number of reads! Evidently, I should decrease the reading time, not use the tag education, and use the tags Towards Data Science and Python." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2018-12-31T23:49:41.082381Z", "start_time": "2018-12-31T23:49:40.913586Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "line": { "color": "black" }, "opacity": 0.8, "size": 12 }, "mode": "markers", "name": "observations", "text": [ "Jupyter Notebook Extensions", "The Copernican Principle and How to Use Statistics to Figure Out How Long Anything Will Last", "Docker for Data Science Without the Hassle", "How to Write a Jupyter Notebook Extension", "How to Create Value with Machine Learning", "Python and Slack: A Natural Match", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Deploying a Python Web App on AWS", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Estimating Probabilities with Bayesian Modeling in Python", "Feature Engineering: What Powers Machine Learning", "Transfer Learning with Convolutional Neural Networks in PyTorch", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "Overcome Your Biases with Data", "Recurrent Neural Networks by Example in Python", "My Weaknesses as a Data Scientist", "Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data", "Neural Network Embeddings Explained", "Building a Recommendation System Using Neural Network Embeddings", "How to Put Fully Interactive, Runnable Code in a Medium Post", "Five Minutes to Your Own Website", "Converting Medium Posts to Markdown for Your Blog", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "Another Machine Learning Walk-Through and a Challenge", "Practical Advice for Data Science Writing", "The most important part of a data science project is writing a blog post", "How to get the right data? 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure, future_df = make_extrapolation(df, 'read_time', years=1, degree=3)\n", "iplot(figure)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions\n", "\n", "Well, that's about all I have! There is a lot of additional analysis that could be done here, and going forward, I'll be further developing these functions and trying to extract more information. Feel free to use these functions on your own articles, and of course, contribute as needed! Developing this library has been enjoyable, and I look forward to expanding it so any suggestions are welcome and appreciated." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "384px" }, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }